The following is a conversation with David Patterson, touring award winner and professor
of computer science at Berkeley. He's known for pioneering contributions to risk processor
architecture used by 99% of new chips today and for co-creating RAID storage. The impact that these
two lines of research and development have had in our world is immeasurable. He's also one of the
great educators of computer science in the world. His book with John Hennessey is how I first learned
about and was humbled by the inner workings and machines at the lowest level.
Quick summary of the ads. Two sponsors, the Jordan Harbinger Show and Cash App. Please
consider supporting the podcast by going to jordanharbinger.com slash lex and downloading
Cash App and using code lexpodcast. Click on the links, buy the stuff. It's the best way to support
this podcast and in general, the journey I'm on in my research and startup. This is the artificial
intelligence podcast. If you enjoy it, subscribe on YouTube, review it with five stars an apple
podcast, support it on Patreon or connect with me on Twitter at Lex Freedman spelled without the E
just F-R-I-D-M-A-N. As usual, I'll do a few minutes of ads now and never any ads in the middle that
can break the flow of the conversation. This episode is supported by the Jordan Harbinger Show. Go
to jordanharbinger.com slash lex. It's how he knows I sent you. On that page, there's links to
subscribe to an apple podcast, Spotify and everywhere else. I've been binging on this podcast.
It's amazing. Jordan is a great human being. He gets the best out of his guests, dies deep,
calls them out when it's needed and makes the whole thing fun to listen to. He's interviewed
Kobe Bryant, Mark Cuban, Neil deGrasse Tyson, Gary Kasparov and many more. I recently listened
to his conversation with Frank Abagnale, author of Catch Me If You Can, one of the world's most
famous conmen. Perfect podcast length and topic for a recent long distance run that I did.
Again, go to jordanharbinger.com slash Lex to give him my love and to support this podcast.
Subscribe also on apple podcast, Spotify and everywhere else.
This show is presented by Cash App, the greatest sponsor of this podcast ever and
the number one finance app in the app store. When you get it, use code lexpodcast. Cash App
lets you send money to friends by Bitcoin and invest in the stock market with as little as $1.
Since Cash App allows you to buy Bitcoin, let me mention that cryptocurrency in the context of
the history of money is fascinating. I recommend Ascent of Money as a great book on this history.
Also, the audiobook is amazing. Debits and credits on ledgers started around 30,000 years ago.
The U.S. dollar created over 200 years ago and the first decentralized cryptocurrency released
just over 10 years ago. So given that history, cryptocurrency is still very much in its early
days of development, but it's still aiming to and just might redefine the nature of money.
So again, if you get Cash App from the app store, Google Play and use the code lexpodcast,
you get $10 and Cash App will also donate $10 to first an organization that is helping to advance
robotics system education for young people around the world. And now here's my conversation with
David Patterson. Let's start with the big historical question. How have computers changed
in the past 50 years at both the fundamental architectural level and in general in your eyes?
Well, the biggest thing that happened was the invention of the microprocessor. So computers
that used to fill up several rooms could fit inside your cell phone. And not only and not
only did they get smaller, they got a lot faster. So they're million times faster than they were
50 years ago. And they're much cheaper and they're ubiquitous. You know,
there's 7.8 billion people on this planet. Probably half of them have cell phones right now,
just remarkable. It's probably more microprocessors than there are people.
Sure. I don't know what the ratio is, but I'm sure it's above one. Maybe it's 10 to one or
some number like that. What is a microprocessor? So a way to say what a microprocessor is is to
tell you what's inside a computer. So a computer forever has classically had five pieces. There's
input and output, which kind of naturally, as you'd expect, is input is like speech or typing
and output is displays. There's a memory and like the name sounds, it remembers things. So it's
integrated circuits whose job is you put the information in, then when you ask for it, it
comes back out. That's memory. And the third part is the processor where the team microprocessor
comes from. And that has two pieces as well. And that is the control, which is kind of the brain
of the processor and the, what's called the arithmetic units, kind of the brawn of the
computer. So if you think of the, as a human body, the arithmetic unit, the thing that does the
number crunching is the, is the body and the control is the brain. So those five pieces, input, output,
memory, arithmetic unit and control are, have been in computers since the very dawn. And the last
two are considered the processor. So a microprocessor simply means a processor that fits on a microchip.
And that was invented about, you know, 40 years ago, was the first microprocessor.
It's interesting that you refer to the arithmetic unit as the, like you connected to the, the body
and the controllers of the brain. So I guess I never thought of it that way. The, the nice way
to think of it, because most of the actions the microprocessor does in terms of literally sort of
computation, but the microprocessor does computation, it processes information. And most of the thing
it does is basic arithmetic, arithmetic operations. What, what are the operations by the way?
It's a lot like a calculator. You know, so there are add instructions, subtract instructions,
multiply and divide. And kind of the brilliance of the invention of the, of the, of the computer
or the processor is that it performs very trivial operations, but it just performs billions of
them per second. And what we're capable of doing is writing software that can take these very trivial
instructions and have them create tasks that can do things better than human beings can do today.
Just looking back through your career, did you anticipate the kind of how good we would be
able to get at doing these small basic operations? Like what, like how many surprises along the way
we just kind of set back and said, wow, I didn't expect it to go this fast, this good. Well, the,
the fundamental driving force is what's Gordon Moore's law, which was named after Gordon Moore,
who's a Berkeley illness. And he made this observation very early in what are called
semiconductors. And semiconductors are these ideas, you can build these very simple switches,
and you can put them on these microchips. And he made this observation over 50 years ago,
he looked at a few years and said, I think what's going to happen is the number of these little
switches called transistors is going to double every year for the next decade. And he said this
in 1965. And in 1975, he said, well, maybe it's going to double every two years. And that what
other people since named that Moore's law guided the industry. And when Gordon Moore made that
prediction, he wrote a paper back in, I think, in the, in the 70s and said, not only did this
going to happen, he wrote, what would be the implications of that? And in this article from
1965, he, he shows ideas like computers being in cars and computers being in something that you
would buy in the grocery store and stuff like that. So he kind of not only called his shot,
he called the implications of it. So if you were in, in the computing field, and if you believed
Moore's prediction, he kind of said what the, what would be happening in the future. So, so it's
not kind of, it's at one sense, this is what was predicted. And you could imagine, it was easy to
believe that Moore's law was going to continue. And so this would be the implications. On the
other side, there are these kind of shocking events in your life. Like I remember driving
in the marine across the Bay in San Francisco and seeing a bulletin board at a local
civic center and had a URL on it. And it was like, for all, for all, for the people at the time,
these first URLs, and that's the, you know, WWW select stuff with the HTTP, people thought it was
look, look like alien, alien writing, right? They'd see these advertisements and commercials
or bulletin boards that had this alien writing on it. So for the laypeople, it was like, what the
hell is going on here? And for those people in industry, it was, oh my God, this stuff is getting
so popular, it's actually leaking out of our nerdy world and into the real world. So that,
I mean, there was events like that. I think another one was, I remember with the, in the early days
of the personal computer, when we started seeing advertisements in magazines for personal computers,
like it's so popular that it's made the newspaper. So at one hand, you know, Gordon Moore predicted
it and you kind of expected it to happen. But when it really hit and you saw it affecting society,
it was, it was shocking. So maybe taking a step back and looking both the engineering and philosophical
perspective, what, what do you see as the layers of abstraction in the computer? Do you see a
computer as a, a set of layers of abstractions? Yeah, I think that's one of the things that
computer science fundamentals is the, these things are really complicated in the way we cope with
complicated software and complicated hardware is these layers of abstraction. And that simply means
that we, you know, suspend disbelief and pretend that the only thing you know is that layer.
And you don't know anything about the layer below it. And that's the way we can make very
complicated things. And probably it started with hardware, that that's the way it was done. But
it's been proven extremely useful. And, you know, I would say in a modern computer today,
there might be 10 or 20 layers of abstraction. And they're all trying to kind of enforce this
contract is all you know is this interface, there's a set of commands that you can are allowed to use
and you stick to those commands and we will faithfully execute that. And it's like peeling
the air layers of a London onion, you get down, there's a new set of layers and so forth. So for
people who want to study computer science, the exciting part about it is you can keep
peeling those layers. You take your first course and you might learn to program in Python and then
you can take a follow on course and you can get it down to a lower level language like C and,
you know, you can go and then you can if you want to you can start getting into the hardware layers
and you keep getting down all the way to that transistor that I talked about that Gordon Moore
predicted. And you can understand all those layers all the way up to the highest level
application software. So it's a very kind of magnetic field. If you're interested,
you can go into any depth and keep going. In particular, what's happening right now or it's
happened in software last 20 years and recently in hardware, there's getting to be open source
versions of all of these things. So what open source means is what the engineer, the programmer
designs. It's not secret that belonging to a company. It's out there on the worldwide web
so you can see it. So you can look at for lots of pieces of software that you use, you can see
exactly what the programmer does if you want to get involved. That used to stop at the hardware.
Recently, there's been an effort to make open source hardware and those interfaces open. So
you can see that. So instead of before you had to stop at the hardware, you can now start going
layer by layer below that and see what's inside there. So it's a remarkable time that for the
interested individual can really see in great depth what's really going on in the computers that
power everything that we see around us. Are you thinking also when you say open source at the
hardware level, is this going to the design architecture instruction set level or is it
going to literally the manufacturer of the actual hardware, of the actual chips,
whether that's ASIC specialized, a particular domain or the general?
Yeah. So let's talk about that a little bit. So when you get down to the bottom layer of
software, the way software talks to hardware is in a vocabulary. And what we call that vocabulary,
we call that the words of that vocabulary called instructions. And the technical term
for the vocabulary is instruction set. So those instructions are likely we talked about earlier
that can be instructions like add, subtract, and multiply, divide. There's instructions to put data
into memory, which is called a store instruction and to get data back, which is called a load
instructions. And those simple instructions go back to the very dawn of computing in 1950,
the commercial computer had these instructions. So that's the instruction set that we're talking
about. So up until I'd say 10 years ago, these instruction sets are all proprietary. So a very
popular one is owned by Intel, the one that's in the cloud and in all the PCs in the world.
Intel owns that instruction set. It's referred to as the x86. There have been a sequence of
ones that the first number was called 8086. And since then, there's been a lot of numbers,
but they all ended in 86. So there's been that kind of family of instruction sets.
And that's proprietary. And that's proprietary. The other one that's very popular is from ARM.
That kind of powers all the cell phones in the world, all the iPads in the world,
and a lot of things that are so-called Internet of Things devices. ARM and that one is also
proprietary. ARM will license it to people for a fee, but they own that. So the new idea that got
started at Berkeley kind of unintentionally 10 years ago is in early in my career, we pioneered
a way to do these vocabulary instruction sets that was very controversial at the time.
At the time, in the 1980s, conventional wisdom was these vocabulary instruction sets should have
powerful instructions. So polysyllabic kind of words, you can think of that. And so that instead
of just add, subtract, and multiply, they would have polynomial divide or sort a list. And the hope
was of those powerful vocabularies that make it easier for software. So we thought that didn't
make sense for microprocessors. There was people at Berkeley and Stanford and IBM who argued the
opposite. And what we called that was a reduced instruction set computer. And the abbreviation
was RISC and typical for computer people, we use the abbreviations as I'm pronouncing it. So risk
was the thing. So we said for microprocessors, which with Gordon's more is changing really fast,
we think it's better to have a pretty simple set of instructions, reduced set of instructions,
that that would be a better way to build microprocessors, since they're going to be changing
so fast due to Moore's law. And then we'll just use standard software to cover the use,
generate more of those simple instructions. And one of the pieces of software that it's in that
software stack, going between these layers of abstractions is called a compiler. And it's
basically translates. It's a translator between levels, we said the translator will handle that.
So the technical question was, well, since they're these reduced instructions, you have to execute
more of them. Yeah, that's right. But maybe execute them faster. Yeah, that's right. They're
simpler, so they go faster, but you have to do more of them. So what's what's that trade off look
like? And it ended up that we ended up executing maybe 50% more instructions, maybe a third more
instructions, but they ran four times faster. So so this risk controversial risk ideas
prove to be maybe factors of three or four better. I love that this idea was controversial and
almost kind of like rebellious. So that's in the context of what was more conventional is the
complex instructional side computing. So how'd you pronounce that? Sisk. Sisk, which is risk versus
sisk. And, and believe it or not, this sounds very, very, you know, who cares about this, right?
It was, it was violently debated at several conferences. It's like, what's the right way to
go is is and people thought risk was, you know, was a de evolution. We're going to make software
worse by making those instructions simpler and their fierce debates at several conferences in
the 1980s. And then later in the 80s, it kind of settled to these benefits. It's not completely
intuitive to me why risk has for the most part one. Yeah. So why did that happen? Yeah. Yeah.
And maybe I can sort of say a bunch of dumb things that could lay the land for further commentary.
So to me, this is a, this is kind of interesting thing. If you look at C plus plus versus C,
with modern compilers, you really could write faster code with C plus plus. So relying on
the compiler to reduce your complicated code into something simple and fast. So to me, comparing
risk, maybe this is a dumb question, but why is it that focusing the definition,
the design of the instruction set on very few simple instructions
in the long run provide faster execution versus coming up with, like you said,
a ton of complicated instructions that over time, you know, years, maybe decades,
you come up with compilers that can reduce those into simple instructions for you.
Yeah. So let's try and split that into two pieces. So if the compiler can do that for you,
if the compiler can take, you know, a complicated program and produce simpler instructions,
then the programmer doesn't care, right? Programmer, I don't care just how, how fast is the computer
I'm using, how much is the cost. And so what we, what happened kind of in the software industry
is right around before the 1980s, critical pieces of software were still written, not in
languages like C or C plus plus, they were written in what's called assembly language,
where there's this kind of humans writing exactly at the instructions at the level
that a computer can understand. So they were writing add, subtract, multiply, you know,
instructions is very tedious. But the belief was to write this lowest level of software that
that people use, which are called operating systems, they had to be written in assembly
language because these high level languages were just too inefficient. They were too slow or the
programs would be too big. So that changed with a famous operating system called UNIX,
which is kind of the grandfather of all the operating systems today. So the UNIX demonstrated
that you could write something as complicated as an operating system in a language like C.
So once that was true, then that meant we could hide the instruction set from the programmer.
And so that meant then it didn't really matter. The programmer didn't have to write
lots of these simple instructions. That was up to the compiler. So that was part of our arguments
for risk is if you were still writing assembly language, there's maybe a better case for sys
constructions. But if the compiler can do that, it's going to be, you know, that's done once
the computer translates at once. And then every time you run the program, it runs at this,
this potentially simpler instructions. And so that that was the debate, right, is because
and people would acknowledge that the simpler instructions could lead to a faster computer.
You can think of model syllabic instructions, you could say them, you know, if you think of
reading, you could probably read them faster or say them faster than long instructions. The same
thing, that analogy works pretty well for hardware. And as long as you didn't have to read a lot more
of those instructions, you could win. So that's, that's kind of that's the basic idea for risk.
But it's interesting that the, in that discussion of Unix and C, that there's only one step of
levels of abstraction from the code that's really the closest to the machine to the code
that's written by human. It's, at least to me, again, perhaps a dumb intuition, but it feels
like there might have been more layers, sort of different kinds of humans stacked up of each other.
Well, so what's true and not true about what you said is several of the layers of software,
like, so the, if you hear two layers would be, suppose we just talked about two layers, that
would be the operating system, like you get from, from Microsoft or from Apple, like iOS or the
Windows operating system. And let's say applications that run on top of it, like Word or Excel. So
both the operating system could be written in C. And the application could be written in C. So,
but you could construct those two layers. And the applications absolutely do call up on the
operating system. And the, the change was that both of them could be written in higher level
languages. So it's one step of a translation, but you can still build many layers of abstraction
of software on top of that. And that's how, how things are done today. So,
still today, many of the layers that you'll, you'll deal with, you may deal with debuggers,
you may deal with linkers. There's libraries, many of those today will be written in C++, say,
even though that language is pretty ancient. And even the Python interpreter is probably written
in C or C++. So lots of layers there are probably written in these some old-fashioned
efficient languages that still take one step to produce these instructions, produce risk
instructions, but they're composed, each layer of software invokes one another through these
interfaces. And you can get 10 layers of software that way. So in general, the risk was developed
here, Berkeley? It was kind of the three places that were these radicals that advocated for this
against the rest of the community were IBM, Berkeley and Stanford. You're one of these radicals.
And how radical did you feel? How confident did you feel? How doubtful were you that risk
might be the right approach? Because it may, you can also into it that is kind of taking
a step back into simplicity, not forward into simplicity. Yeah, no, it was easy to make.
Yeah, it was easy to make the argument against it. Well, this was my colleague John Hennessy at
Stanford and I, we were both assistant professors. And for me, I just believed in the power of our
ideas. I thought what we were saying made sense. Morse law is going to move fast.
The other thing that I didn't mention is one of the surprises of these complex instruction sets.
You could certainly write these complex instructions if the programmers writing them
themselves. It turned out to be kind of difficult for the compiler to generate those complex
instructions kind of ironically, you'd have to find the right circumstances that that just exactly
fit this complex instruction, it was actually easier for the compiler to generate these simple
instructions. So not only did these complex instructions make the hardware more difficult
to build, often the compiler wouldn't even use them. And so it's harder to build the compiler
doesn't use them that much. The simple instructions go better with Morse law that, you know, the
number of transistors is doubling every, every two years. So we're going to have, you know,
you want to reduce the time to design the microprocessor, that may be more important
than the number of instructions. So I think we believed in the, that we were right, that this
was the best idea. Then the question became in these debates, well, yeah, that's a good technical
idea. But in the business world, this doesn't matter. There's other things that matter. It's
like arguing that if there's a standard with the railroad tracks, and you've come up with a
better with, but the whole world is covered in railroad tracks, so you'll, your ideas have no
chance of success, commercial success, it was technically right. But commercially, it'll be
insignificant. Yeah, it's kind of sad that this world, the history of human civilization is full
of good ideas that lost because somebody else came along first with a worse idea. And it's good
that in the computing world, at least some of these have, well, you could, I mean, there's probably
still CISC people that say, yeah, there's still a lot. And what happened was what was interesting,
Intel, a bunch of the CISC companies with CISC instruction sets of vocabulary,
they gave up, but not Intel. What Intel did to its credit, because Intel's vocabulary was
in the personal computer. And so that was a very valuable vocabulary, because the way we distribute
software is in those actual instructions. It's in the instructions of that instruction set. So
they, you don't get that source code, what the programmers wrote, you get, after it's been
translated into the list level, that's if you were to get a floppy disk or download software,
it's in the instructions of that instruction set. So the x86 instruction set was very valuable.
So what Intel did cleverly, and amazingly, is they had their chips in hardware do a translation
step. They would take these complex instructions and translate them into essentially in-risk
instructions in hardware on the fly at gigahertz clock speeds. And then any good idea that risk
people had, they could use, and they could still be compatible with this really valuable PC
software base, which also had very high volumes, 100 million personal computers per year. So
the CISC architecture in the business world was actually one in this PC era.
So just going back to the time of designing risk. When you design an instruction set
architecture, do you think like a programmer? Do you think like a microprocessor engineer?
Do you think like a artist, a philosopher? Do you think in software and hardware? I mean,
is it art? Is it science? Yeah, I'd say I think designing a good instruction set is an art.
And I think you're trying to balance the simplicity and speed of execution with how well
easy it will be for compilers to use it. You're trying to create an instruction set
that everything in there can be used by compilers. There's not things that are missing,
that'll make it difficult for the program to run. They run efficiently, but you want it to be easy
to build as well. So you're thinking, I'd say you're thinking hardware, trying to find a hardware
software compromise that'll work well. And it's a matter of taste. It's kind of fun to build
instruction sets. It's not that hard to build an instruction set, but to build one that catches on
and people use, you have to be fortunate to be the right place at the right time or have a design
that people really like. Are you using metrics? Is it quantifiable? Because you kind of have to
anticipate the kind of programs that people will write ahead of time. So can you use numbers?
Can you use metrics? Can you quantify something ahead of time? Or is this again, that's the
art part where you're kind of anticipating? No, it's a big change, kind of what happened.
I think from Hennessy's and my perspective in the 1980s, what happened was going from kind of
really, you know, taste and hunches to quantifiable. And in fact, he and I wrote a textbook at the
end of the 1980s called Computer Architecture, A Quantitative Approach. I heard of that.
And it's the thing, it had a pretty big impact in the field because we went from
textbooks that kind of listed. So here's what this computer does, and here's the pros and cons,
and here's what this computer does in pros and cons, to something where there were formulas
and equations where you could measure things. So specifically for instruction sets,
what we do in some other fields do is we agree upon a set of programs, which we call benchmarks,
and a suite of programs. And then you develop both the hardware and the compiler, and you get
numbers on how well your computer does, given its instruction set and how well you implemented it
in your microprocessor and how good your compilers are. And in computer architecture, we, you know,
using professor's terms, we grade on a curve rather than grade on an absolute scale. So when
you say, you know, these programs run this fast, well, that's kind of interesting, but how do you
know it's better while you compare it to other computers at the same time? So the best way we
know how to make, turn it into a kind of more science and experimental and quantitative is
to compare yourself to other computers of the same era that have the same access, the same kind of
technology on commonly agreed benchmark programs. So maybe to toss up two possible directions,
we can go one is what are the different tradeoffs in designing architectures? We've been already
talking about Cisco risk, but maybe a little bit more detail in terms of specific features that
you were thinking about. And the other side is, what are the metrics that you're thinking about
when looking at these tradeoffs? Yeah, let's talk about the metrics. So during these debates,
we actually had kind of a hard time explaining, convincing people the ideas. And partly, we
didn't have a formula to explain it. And a few years into it, we hit upon a formula that helped
explain what was going on. And I think if we can do this, see how it works orally. So the
way is if I can do a formula orally. So fundamentally, the way you measure performance
is how long does it take a program to run? Program, if you have 10 programs, and typically these
benchmarks were sweet, because you'd want to have 10 programs so they could represent lots of
different applications. So for these 10 programs, how long does it take to run? When now, when you're
trying to explain why it took so long, you could factor how long it takes a program to run into
three factors. One of the first one is how many instructions did it take to execute? So that's
the, that's the what we've been talking about, you know, the instructions of a chemie. How many
did it take? All right. The next question is how long did each instruction take to run on average?
So you'd multiply the number of instructions times how long it took to run. And that gives you
how time. Okay, so that's, but now let's look at this metric of how long did it take the instruction
to run? Well, it turns out the way we could build computers today is they all have a clock. And you've
seen this when you, if you buy a microprocessor, it'll say 3.1 gigahertz or 2.5 gigahertz and more
gigahertz is good. Well, what that is, is the speed of the clock. So 2.5 gigahertz turns out to be
four billionths of instruction or four nanoseconds. So that's the clock cycle time. But there's another
factor, which is what's the average number of clock cycles that takes per instruction? So it's
number of instructions, average number of clock cycles in the clock cycle time. So in these risks
system debates, we would, we, they would concentrate on, but risk needs to take more instructions.
And we'd argue what maybe the clock cycle is faster, but what the real big difference was,
was the number of clock cycles per instruction instruction. That's fascinating. What about the
mess of, the beautiful mess of parallelism in the whole picture? Parallelism, which has to do with,
say, how many instructions could execute in parallel and things like that. You could think of that as
affecting the clock cycles per instruction, because it's the average clock cycles per instruction.
So when you're running a program, if it, if it took a hundred billion instructions and on average,
it took two clock cycles per instruction, and they were four nanoseconds, you could multiply
that out and see how long it took to run. And there's all kinds of tricks to try and reduce the
number of clock cycles per instruction. But it turned out that the way they would do these
complex instructions is they would actually build what we would call an interpreter in a, in a
simpler, a very simple hardware interpreter. But it turned out that for the SISC instructions,
if you had to use one of those interpreters, it would be like 10 clock cycles per instruction,
where the risk instructions could be two. So there'd be this factor of five advantage in clock
cycles per instruction. We have to execute, say, 25 or 50% more instructions. So that's where the
wind would come. And then you could make an argument whether the clock cycle times are the
same or not. But pointing out that we could divide the benchmark results time per program into three
factors. And the biggest difference in risk and SISC was the clock cycles per, you execute a few
more instructions, but the clock cycles per instruction is much less. And that was what this
debate was. Once we made that argument, then people say, Oh, okay, I get it. And so we went from,
it was outrageously controversial in, you know, 1982, that maybe probably by 1984. So people said,
Oh, yeah, technically, they've got a good argument. What are the instructions in the risk
instruction set just to get an intuition? Okay, 1995, I was asked to sign to predict the future
of what microprocessor of the future. So I, and that, as I'd seen these predictions, and usually
people predict something outrageous, just to be entertaining, right? And so my prediction for 2020
was, you know, things are going to be pretty much, they're going to look very familiar to what they
are, and they are. And if you were to read the article, you know, the things I said are pretty
much true. The instructions that that have been around forever are kind of the same.
And that's the outrageous prediction, actually, given how fast computers have been growing.
Oh, and, you know, Morse law was going to go on, we thought for 25 more years, you know,
who knows. But kind of the surprising thing, in fact, you know, Hennessy and I, you know, won
the ACM AM Touring Award for both the risk instruction set contributions and for that
textbook I mentioned. But, you know, we're surprised that here we are 35, 40 years later,
after we did our work. And the conventionalism of the best way to do instruction sets is still
those risk instruction sets that looked very similar to what we looked like we did in the 1980s. So
those surprisingly, there hasn't been some radical new idea, even though we have, you know, a million
times as many transistors as we had back then. But what are the basic constructions and how
did they change over the years? So we're talking about addition, subtract, these are the specific,
so the, so the things that are in a calculator, you are in a computer. So any of the buttons that
are in the calculator in the computer. So the, the button, so if there's a memory function key,
and like I said, those are turns into putting something in memories called a store, bring
something back to a load, just a quick tangent. When you say memory, what does memory mean?
Well, I told you there were five pieces of a computer. And if you remember in a calculator,
there's a memory key. So you want to have intermediate calculation and bring it back
later. So you'd hit the memory plus key M plus, maybe, and it would put that into memory. And
then you'd hit an RM like recurrent instruction, and then bring it back on the display. So you
don't have to type it, you don't have to write it down and bring it back again. So that's exactly
what memory is. You can put things into it as temporary storage and bring it back when you need
it later. So that's memory and loads and stores. But the big thing, the difference between a computer
and a calculator is that the computer can make decisions. And, and amazingly, decisions are
as simple as, is this value less than zero? Or is this value bigger than that value? So there's
those instructions, which are called conditional branch instructions, is what give computers
all its power. If you were in the early days of computing before what's called the general
purpose microprocessor, people would write these instructions kind of in the hardware.
And, but it couldn't make decisions, it would just, it would do the same thing over and over again.
With the power of having branch instructions, it can look at things that make decisions
automatically. And it can make these decisions, you know, billions of times per second. And
amazingly enough, we can get, you know, thanks to advanced machine learning, we can, we can create
programs that can do something smarter than human beings can do. But if you go down that very basic
level, it's the instructions are the keys on the calculator, plus the ability to make decisions
of these conditional branch instructions. And all decisions fundamentally can be reduced down to
these branch instructions. Yeah. So in fact, and so, you know, going way back in the stack, back to,
you know, we did four risk projects at Berkeley in the 1980s, they did a couple at Stanford
in the 1980s. In 2010, we decided we wanted to do a new instruction set, learning from the mistakes
of those risk architecture in the 1980s. And that was done here at Berkeley, almost exactly 10 years
ago. And the people who did it, I participated, but other Krzysztof Sanovic and others drove it.
They called it risk five to honor those risks, the four risk projects of the 1980s.
So what does risk five involve? So risk five is another instruction set of vocabulary,
it's learned from the mistakes of the past, but it still has, if you look at the, there's a core
set of instructions, it's very similar to the simplest architectures from the 1980s. And the big
difference about risk five is it's open. So I talked early about proprietary versus open and
open source software. So this is an instruction set. So it's a vocabulary, it's not, it's not
hardware. But by having an open instruction set, we can have open source implementations,
open source processors that people can use. Where do you see that going? It's a really exciting
possibilities, but you're just like in the scientific American, if you were to predict
10, 20, 30 years from now, that kind of ability to utilize open source
instruction set architectures like risk five, what kind of possibilities might that unlock?
Yeah. And so just to make it clear, because this is confusing, the specification of risk five is
something that's like in a textbook, there's books about it. So that's what that's kind of defining
an interface. There's also the way you build hardware is you write it in languages, they're
kind of like C, but they're specialized for hardware that gets translated into hardware.
And so these implementations of this specification are what are the open source. So they're written
in something that's called Verilog or VHDL, but it's put up on the web just like you can see the
C++ code for Linux on the web. So that's the open instruction set enables open source implementations
of risk five. They can literally build a processor using this instruction set.
People are, people are. So what happened to us that the story was this was developed here for
our use to do our research. And we made it, we licensed under the Berkeley software distribution
license, like a lot of things get licensed here. So other academics use it, they wouldn't be afraid
to use it. And then about 2014, we started getting complaints that we were using it in our research
and in our courses. And we got complaints from people in industries. Why did you change your
instruction set between the fall and the spring semester? And well, we get complaints from
industrial time. Why the hell do you care what we do with our instruction set? And then when we
talked to them, we found out there was this thirst for this idea of an open instruction set
architecture. And they had been looking for one, they stumbled upon ours at Berkeley, thought it
was, boy, this looks great. We should use this one. And so once we realized there is this need for
an open instruction set architecture, we thought, that's a great idea. And then we started supporting
it and tried to make it happen. So this was kind of kind of we accidentally stumbled into this,
and to this need and our timing was good. And so it's really taking off. There's,
you know, universities are good at starting things, but they're not good at sustaining things. So
like Linux has a Linux foundation, there's a risk five foundation that we started. There's an
annual conferences. And the first one was done, I think, January of 2015. And the one that was
just last December in it, you know, it had 50 people at it. And this one last December had,
I know, 1700 people were at it. And the company's excited all over the world. So if predicting into
the future, you know, if we were doing 25 years, I would predict that risk five will be, you know,
possibly the most popular instruction set architecture out there, because it's a pretty
good instruction set architecture, and it's open and free. And there's no reason lots of people
shouldn't use it. And there's benefits just like Linux is so popular today, compared to 20 years
ago. And, you know, the fact that you can get access to it for free, you can modify it, you
can improve it for all those same arguments. And so people collaborate to make it a better system
for everybody to use. And that works in software. And I expect the same thing will happen in hardware.
So if you look at the arm Intel MIPS, if you look at just the lay of the land. And what do you think,
just for me, because I'm not familiar, how difficult this kind of transition would,
how much challenges this kind of transition would entail. Do you see, let me ask my dumb question
in another way. No, I know where you're headed. Well, there's a bunch. I think the thing you
point out, there's these very popular proprietary instruction sets, the x86. And so how do we
move to risk five potentially, in sort of in the span of five, 10, 20 years, a kind of unification,
given that the devices, the kind of way we use devices, IoT, mobile devices, and the cloud
is keeps changing. Well, part of it, a big piece of it is the software stack. And what right now,
looking forward, there seem to be three important markets. There's the cloud. And the cloud is simply
companies like Alibaba and Amazon and Google, Microsoft, having these giant data centers with
tens of thousands of servers, and maybe a hundred of these data centers all over the world. And
that's what the cloud is. So the computer that dominates the cloud is the x86 instruction set.
Instruction sets used in the cloud of the x86, almost 100% of that today is x86.
The other big thing are cell phones and laptops. Those are the big things today. I mean, the PC
is also dominated by the x86 instruction set, but those sales are dwindling. There's maybe
200 million PCs a year, and there's, is there one and a half billion phones a year? There's numbers
like that. So for the phones, that's dominated by ARM. And now, and a reason that I talked about
the software stacks, and the third category is Internet of Things, which is basically embedded
devices, things in your cars and your microwaves everywhere. So what's different about those
three categories is for the cloud, the software that runs in the cloud is determined by these
companies, Alibaba, Amazon, Google, Microsoft. So they control that software stack. For the cell
phones, there's both for Android and Apple, the software they supply, but both of them have
marketplaces where anybody in the world can build software. And that software is translated or,
you know, compiled down and shipped in the vocabulary of ARM. So that's what's referred to
as binary compatible, because the actual, it's the instructions are turned into numbers, binary
numbers and shipped around the world. So. And so just a quick interruption. So ARM, what is ARM?
Is ARM as an instruction set, like a risk-based? Yeah, it's a risk-based instruction set. It's
a proprietary one. ARM stands for Advanced Risk Machine. ARM is the name where the company is.
So it's a proprietary risk architecture. So, and it's been around for a while, and it's,
you know, surely the most popular instruction set in the world right now. Every year,
billions of chips are using the ARM design in this post-PC era. Was it one of the early
risk adopters of the risk? Yeah. The first ARM goes back, I don't know, 86 or so. So
Berkeley instead did their work in the early 80s. The ARM guys needed an instruction set,
and they read our papers, and it heavily influenced them. So getting back to my story,
what about Internet of Things? Well, software is not shipped in Internet of Things. It's the
embedded device. People control that software stack. So the opportunities for risk five,
everybody thinks, is in the Internet of Things, embedded things, because there's no dominant
player like there is in the cloud or the smartphones. And, you know, it doesn't have a lot of licenses
associated with, and you can enhance the instruction set if you want. And people
have looked at instruction sets and think it's a very good instruction set. So it appears to be
very popular there. It's possible that in the cloud, people, those companies control their
software stacks so that it's possible that they would decide to use risk five if we're talking
about 10 and 20 years in the future. One of the harder would be the cell phones since people
ship software in the ARM instruction set. That would, you'd think, be the more difficult one.
But if risk five really catches on, in a period of a decade, you can imagine that's changing over
too. Do you have a sense why risk five or ARM has dominated? You mentioned these three categories.
Why did ARM dominate? Why does it dominate the mobile device space? And maybe my naive intuition
is that there are some aspects of power efficiency that are important that somehow come along with
risk. Well, part of it is for these old SIS construction sets like in the x86,
it was more expensive to these for, you know, they're older. So they have disadvantages in them
because they were designed 40 years ago. But also they have to translate in hardware from SIS
constructions to risk constructions on the fly. And that costs both silicon area that the chips are
bigger to be able to do that. And it uses more power. So ARM has, which has, you know, followed
this risk philosophy is seen to be much more energy efficient. And in today's computer world,
both in the cloud and cell phone and other things, the limiting resource isn't the number of
transistors you can fit in the chip. It's what how much power can you dissipate for your application.
So by having a reduced instruction set, you that's possible to have a simpler hardware,
which is more energy efficient and energy efficiency is incredibly important in the
cloud when you have tens of thousands of computers in a data center, you want to have the most
energy efficient ones there as well. And of course, for embedded things running off of batteries,
you want those to be energy efficient in the cell phones too. So I think it's believed that
there's a energy disadvantage of using these more complex instruction set architectures.
So the other aspect of this is if we look at Apple, Qualcomm, Samsung, Huawei, all use the
ARM architecture. And yet the performance of the systems varies. I mean, I don't know
whose opinion you take on, but you know, Apple for some reason seems to perform better in terms
of these implementations architecture. So where's the magic enter the picture?
How's that happen? Yeah. So what ARM pioneered was a new business model is they said, well,
here's our proprietary instruction set. And we'll give you two ways to do it.
We'll give you one of these implementations written in things like C called Verilog. And
you can just use ours. Well, you have to pay money for that. Not only will give you their,
you know, will license you to do that, or you could design your own. And so we're talking
about numbers like tens of millions of dollars to have the right to design your own since they
it's the instruction set belongs to them. So Apple got one of those the right to build their own.
Most of the other people who build like Android phones just get one of the designs from ARM to
do it themselves. So Apple developed a really good microprocessor design team. They, you know,
acquired a very good team that had was building other microprocessors and brought them into
the company to build their designs. So the instructions sets are the same, the specifications
are the same, but their hardware design is much more efficient than I think everybody else's.
And that's given Apple an advantage in the marketplace in that the iPhones tend to be the
faster than most everybody else's phones that are there.
It'd be nice to be able to jump around and kind of explore different little sides of this.
But let me ask one sort of romanticized question. What to you is the most beautiful aspect or idea
of risk instruction set or instruction sets or this? Yeah, well, I think, you know, I'm,
you know, I, I was always attracted to the idea of, you know, smallest beautiful is that
the temptation in engineering, it's kind of easy to make things more complicated. It's harder to
come up with a, it's more difficult surprisingly to come up with a simple elegant solution. And I
think that there's a bunch of small features of risk in general that, you know, where you can see
this examples of keeping it simpler, makes it more elegant, specifically in risk five, which,
you know, I'm, I was kind of the mentor in the program, but it was really driven by Christos
Sanovich and two grad students, Andrew Waterman, Yensip Lee, is they hit upon this idea of having
a subset of instructions, a nice simple structure, subset instructions like 40 ish
instructions that all software, the software staff risk five can run just on those 40 instructions.
And then they provide optional features that could accelerate the performance instructions
that if you needed them could be very helpful, but you don't need to have them. And that,
that's a new, really a new idea. So risk five has right now, maybe five optional subsets that
you can pull in, but the software runs without them. If you just want to build the, just the,
the core 40 instructions, that's fine, you can do that. So this is fantastic for educationally is
you can explain computers, you only have to explain 40 instructions and not thousands of them.
Also, if you invent some wild and crazy new technology, like, you know, biological computing,
you'd like a nice simple instruction set, and you can risk five if you implement those core
instructions, you can run, you know, really interesting programs on top of that. So this idea
of a core set of instructions that the software stack runs on, and then optional features that
if you turn them on the compilers were used, but you don't have to, I think is a powerful idea.
What's happened in the past for the proprietary instruction sets is when they add new instructions,
it becomes required piece. And so that all, all microprocessors in the future have to use those
instructions. So it's kind of like, for a lot of people as they get older, they gain weight,
right? It's the weight in age or correlated. And so you can see these instruction sets get
getting bigger and bigger as they get older. So risk five, you know, lets you be as slim as you
as a teenager, and you only have to add these extra features if you're really going to use them
rather than every, you have no choice, you have to keep growing with the instruction set.
I don't know if the analogy holds up, but that's a beautiful notion that there's,
it's almost like a nudge towards here's the simple core, that's the essential.
Yeah. And I think the surprising thing is still, if we, if we brought back, you know, the pioneers
from the 1950s and showed them the instruction set architectures, they'd understand it.
They, they say, wow, that doesn't look that different. Well, you know, I'm surprised.
And it's, there's, it may be something, you know, to talk about philosophical things,
I mean, there may be something powerful about those, you know, 40 or 50 instructions that
all you need is these commands like these instructions that we talked about.
And that is sufficient to build, to bring about, you know, artificial intelligence.
And so it's a remarkable surprising to me that is complicated as it is to build these things,
you know, a microprocessors where the line widths are narrower than the wavelength of light,
you know, is this amazing technologies at some fundamental level.
The commands that software executes are really pretty straightforward and haven't changed that
much in, in decades, which what a surprising outcome.
So underlying all computation, all touring machines, all artificial intelligence systems,
perhaps might be a very simple instruction set like, like a risk five or it's, yeah,
I mean, that's kind of what I said. I was interested to see, I had another more senior
faculty colleague and he, he had written something in Scientific American and, you know,
his 25 years in the future and his turned out about when I was a young professor and he said,
yep, I checked it. I was, I was interested to see how that was going to turn out for me.
And it's pretty held up pretty well. But yeah, so there's, there's probably, there's some, I,
you know, there's, there must be something fundamental about those instructions that were
capable of creating, you know, intelligence from pretty primitive operations and just
doing them really fast. You kind of mentioned a different, maybe radical computational medium
like biological, and there's other ideas. So there's a lot of spaces in ASIC, sort of domain
specific, and then there could be quantum computers and so we can think of all of those
different mediums and types of computation. What's the connection between swapping out different
hardware systems and the instruction set? Do you see those as disjoint or are they fundamentally
coupled? Yeah. So what's, so kind of, if we go back to the history, you know, when Morris Law is in
full effect and you're getting twice as many transistors every couple of years, you know,
kind of the challenge for computer designers is how can we take advantage of that? How can we turn
those transistors into better computers faster typically? And so there was an era, I guess in
the 80s and 90s where computers were doubling performance every 18 months. And if you weren't
around then, what would happen is you had your computer and your friend's computer, which was
like a year or a year and a half newer, and it was much faster than your computer. And he or she
could get their work done much faster than your time consumers. So people took their computers,
perfectly good computers, and threw them away to buy a newer computer because the computer
one or two years later was so much faster. So that's what the world was like in the 80s and 90s.
Well, with the slowing down of Morris Law, that's no longer true, right? Now with that
desktop computers with the laptops, I only get a new desktop when it breaks, right? I'll damn the
disk broke or this display broke, I got to buy a new computer. But before you would throw them
away because they were just so sluggish compared to the latest computers. So that's, you know,
that's a huge change of what's gone on. So but since this lasted for decades, kind of programmers,
and maybe all of society is used to computers getting faster regularly. We now believe those of
us who are in computer design, it's called computer architecture, that the path forward
is instead is to add accelerators that only work well for certain applications. So since
Morris Law is slowing down, we don't think general purpose computers are going to get
a lot faster. So the Intel processes of the world are not going to have them been getting a lot
faster. They've been barely improving like a few percent a year. It used to be doubling every
18 months, and now it's doubling every 20 years. So it was just shocking. So to be able to deliver
on what Morris Law used to do, we think what's going to happen, what is happening right now is
people adding accelerators to their microprocessors that only work well for some domains. And by sheer
coincidence, at the same time that this is happening, has been this revolution in artificial
intelligence called machine learning. So as I'm sure your other guests have said, AI had these two
competing schools of thought is that we could figure out artificial intelligence by just writing
the rules top down, or that was wrong. You had to look at data and infer what the rules are,
the machine learning, and what's happened in the last decade or eight years as machine learning
has won. And it turns out that machine learning, the hardware you build for machine learning is
pretty much multiply. The matrix multiply is a key feature for the way people machine learning is
done. So that's a godsend for computer designers. We know how to make matrix multiply run really
fast. So general purpose microprocessors are slowing down. We're adding accelerators for
machine learning that fundamentally are doing matrix multiplies much more efficiently than
general purpose computers have done. So we have to come up with a new way to accelerate things.
The danger of only accelerating one application is how important is that application turns
it turns like machine learning gets used for all kinds of things. So serendipitously,
we found something to accelerate that's widely applicable. And we don't even we're in the middle
of this revolution of machine learning, we're not sure what the limits of machine learning are.
So this has been kind of a godsend. If you're going to be able to excel deliver on improved
performance, as long as people are moving their programs to be embracing more machine learning,
we know how to give them more performance, even as Moore's law is slowing down.
And counterintuitively, the machine learning mechanism, you can say is domain specific,
but because it's leveraging data, it's actually could be very broad in terms of
in terms of the domains that could be applied in. Yeah, that's exactly right.
Sort of it's almost sort of people sometimes talk about the idea of software 2.0, we're almost
taking another step up in the abstraction layer in designing machine learning systems,
because now you're programming in the space of data in the space of hyper parameters,
it's changing fundamentally the nature of programming. And so the specialized devices
that that accelerate the performance, especially neural network based machine learning systems
might become the new general. Yeah, so the this thing that's interesting point out these are
not coral, these are not tied together. The enthusiasm about machine learning about
creating programs driven from data that we should figure out the answers from data rather than
kind of top down, which classically the way most programming is done and the way artificial
intelligence used to be done, that's a movement that's going on at the same time. Coincidentally,
and the first word in machine learning is machines, right? So that's going to increase
the demand for computing, because instead of programmers being smart writing those things
down, we're going to instead use computers to examine a lot of data to kind of create the
programs. That's the idea. And remarkably, this gets used for all kinds of things very successfully,
the image recognition, the language translation, the game playing, and you know, it gets into
pieces of the software stack, like databases and stuff like that, we're not quite sure
how general purpose it is, but that's going on independent of this hardware stuff.
What's happening on the hardware side is Moore's law is slowing down right when we need a lot more
cycles. It's failing us right when we need it, because there's going to be a greater increase
in computing. And then this idea that we're going to do so-called domain specific, here's a domain
that your greatest fear is you'll make this one thing work, and that'll help, you know,
5% of the people in the world. Well, this looks like it's a very general purpose thing. So the
timing is fortuitous that if we can, perhaps if we can keep building hardware that will accelerate
machine learning, the neural networks, that'll beat the timing will be right that that neural
network revolution will transform your software, the so-called software 2.0. And the software of
the future will be very different from the software of the past. And just as our microprocessors,
even though we're still going to have that same basic risk instructions to run a big pieces of
the software stack like user interfaces and stuff like that, we can accelerate the kind of the small
piece that's computationally-impensive. It's not lots of lines of code, but it takes a lot of
cycles to run that code, that that's going to be the accelerator piece. And so that's what makes this
from a computer designer's perspective a really interesting decade. But Hennessy and I talked
about in the title of our Turing-Warned speech is a new golden age. We see this as a very exciting
decade, much like when we were assistant professors and the risk stuff was going on. That was a very
exciting time was where we were changing what was going on. We see this happening again, tremendous
opportunities of people because we're fundamentally changing how software is built and how we're
running it. So which layer of the abstraction do you think most of the acceleration might be
happening? If you look in the next 10 years, Google is working on a lot of exciting stuff with
the TPU. So if there's a closer to the hardware, there could be optimizations around the
closer to the instruction set, there could be optimization at the compiler level, it could
be even at the higher level software stack. Yeah, it's going to be, if you think about the old
Resist Debate, it was software hardware. It was the compilers improving as well as the architecture
improving and that's likely to be the way things are now. With machine learning, they're using
domain specific languages. The languages like TensorFlow and PyTorch are very popular with
the machine learning people. Those are raising the level of abstraction. It's easier for people
to write machine learning in these domain specific languages like PyTorch and TensorFlow.
So where the most optimization might happen? Yeah, and so there'll be both the compiler piece
and the hardware piece underneath it. So as you kind of the fatal flaw for hardware people is to
create really great hardware, but not have brought along the compilers. What we're seeing right now
in the marketplace because of this enthusiasm around hardware for machine learning is getting
probably billions of dollars invested in startup companies. We're seeing startup companies go belly
up because they focused on the hardware but didn't bring the software stack along. We talked about
benchmarks earlier. So I participated in machine learning. Didn't really have a set of benchmarks.
I think just two years ago, they didn't have a set of benchmarks and we've created something
called ML Perf, which is machine learning benchmark suite. And pretty much the companies
who didn't invest in the software stack couldn't run ML Perf very well. And the ones
who did invest in the software stack did. And we're seeing kind of in computer architecture,
this is what happens. You have these arguments about risk versus sys. People spend billions of
dollars in the marketplace to see who wins. It's not a perfect comparison, but it kind of sorts
things out. And we're seeing companies go out of business and then companies like there's a company
in Israel called Habana. They came up with machine learning accelerators. They had good ML Perf scores.
Intel had acquired a company earlier called Nirvana a couple years ago. They didn't reveal
their ML Perf scores, which was suspicious. But a month ago, Intel announced that they're
canceling the Nirvana product line and they've bought Habana for $2 billion and Intel's going
to be shipping Habana chips, which have hardware and software and run the ML Perf programs pretty
well. And that's going to be their product line in the future. Brilliant. So maybe just a link
or briefly ML Perf. I love metrics. I love standards that everyone can gather around.
What are some interesting aspects of that portfolio of metrics? Well, one of the interesting
metrics is what we thought. I was involved in the start that Peter Mattson is leading the effort
from Google. Google got it off the ground, but we had to reach out to competitors and say,
there's no benchmarks here. We think this is bad for the field. It'll be much better if we look
at examples like in the risk days, there was an effort to create a for the people in the risk
community got together. Competitors got together are building risk microprocessors degree on a set
of benchmarks that were called spec. And that was good for the industry. It's rather before
the different risk architectures were arguing, well, you can believe my performance others,
but those other guys are liars. And that didn't do any good. So we agreed on a set of benchmarks.
And then we could figure out who was faster between the various risk architectures, but it
was a little bit faster. But that grew the market rather than people were afraid to buy anything.
So we argued the same thing would happen with ML Perf. Companies like Nvidia were maybe worried
that it was some kind of trap. But eventually, we all got together to create a set of benchmarks
and do the right thing, right? And we agree on the results. And so we can see whether TPUs or GPUs
or CPUs are really faster and how much the faster. And I think from an engineer's perspective,
as long as the results are fair, you can live with it. Okay, you kind of tip your hat to your
colleagues at another institution, boy, they did a better job than this. What you what you hate is
if it's, it's false, right? They're making claims and it's just marketing bullshit. And, you know,
and that's affecting sales. So you from an engineer's perspective, as long as it's a fair
comparison, and we don't come in first place, that's too bad, but it's fair. So we wanted to
create that environment for ML Perf. And so now there's 10 companies, I mean, 10 universities
and 50 companies involved. So pretty much ML Perf has is this is the way you measure machine
learning performance. And and it didn't exist even two years ago. One of the cool things
that I enjoy about the internet has a few downsides. But one of the nice things is
people can see through BS a little better with the presence of these kinds of metrics. It's
so it's really nice, companies like Google and Facebook and Twitter. Now, it's the cool thing
to do is to put your engineers forward and to actually show off how well you do on these metrics.
There's not sort of it. Well, there's less of a desire to do marketing less. So am I am I sort
of naive? No, I think I was trying to understand that, you know, what's changed from the 80s in
this era? I think because of things like social networking, Twitter and stuff like that, if you
if you put up, you know, bullshit stuff, right, that's just, you know, mis purposely misleading,
you know, that you can get a violent reaction and social media pointing out the flaws in your
arguments. Right. And so from a marketing perspective, you have to be careful today that
you didn't have to be careful that there'll be people who put out the flaw. You can get the word
out about the flaws and what you're saying much more easily today than in the past. You used to be
it used to be easier to get away with it. And the other thing that's been happening in terms of
showing off engineers is just in the software side, people have largely embraced open source
software. But it was 20 years ago, it was a dirty word at Microsoft. And today, Microsoft is one
of the big proponents of open source software. The kind of that's the standard way most software
gets built, which really shows off your engineers, because you can see, if you look at the source
code, you can see who are making the commits, who's making the improvements, who are the
engineers at all these companies, who are really great programmers and engineers and making really
solid contributions, which enhances their reputations and the reputation of the companies.
But that's of course not everywhere, like in the space that I work more in is autonomous vehicles.
The machinery of hype and marketing is still very strong there, and there's less
willingness to be open in this kind of open source way and sort of benchmark. So ML Perf
is represents the machine learning world is much better at being open source about holding
itself to standards of different, the amount of incredible benchmarks in terms of the different
computer vision, natural language processing tasks is incredible. Historically, it wasn't
always that way. I had a graduate student working with me, David Martin. So in computer, in some
fields, benchmarking is been around forever. So computer architecture, databases, maybe operating
systems, benchmarks are the way you measure progress. But he was working with me and then
started working with Jitendra Malik, and he's Jitendra Malik and computer vision space. I guess
you've interviewed Jitendra. And David Martin told me they don't have benchmarks. Everybody has
their own vision algorithm in the way that, here's my image, look at how well I do, and everybody
had their own image. So David Martin, back when he did his dissertation, figured out a way to do
benchmarks. He had a bunch of graduate students identify images, and then ran benchmarks to see
which algorithms run well. And that was, as far as I know, kind of the first time people did
benchmarks in computer vision, and which was predated all, you know, the things that eventually
led to ImageNet and stuff like that. But then, you know, the vision community got religion. And
then once we got as far as ImageNet, then that let the guys in Toronto be able to win the ImageNet
competition. And then, you know, that changed the whole world. It's a scary step, actually,
because when you enter the world of benchmarks, you actually have to be good to participate,
as opposed to, yeah, you can just, you just believe you're the best in the world.
And I think the people, I think they weren't purposely misleading. I think if you don't have
benchmark timing, how do you know, you know, you could have your intuition is kind of like the way
we did just do computer architecture. Your intuition is that this is the right instruction
set to do this job. I believe in my experience, my hunch is that's true. We had to get to make
things more quantitative to make progress. And so I just don't know how, you know, in fields that
don't have benchmarks, I don't understand how they figure out how they're making progress.
We're kind of in the vacuum tube days of quantum computing. What are your thoughts
in this wholly different kind of space of architectures?
You know, I actually, you know, quantum computing is ideas been around for a while,
and I actually thought, well, I sure hope I retire before I have to start teaching this.
I'd say, because I talk about, give these talks about the slowing of Morris Law, and, you know,
when we need to change by doing domain specific accelerators, a common question, say, what
about quantum computing? The reason that comes up, it's in the news all the time. So I think to keep
and the third thing to keep in mind is quantum computing is not right around the corner.
There have been two national reports, one by the National Academy of Engineering and other by
the computing consortium, where they did a frank assessment of quantum computing, and
both of those reports said, you know, as far as we can tell, before you get air-corrected
quantum computing, it's a decade away. So I think of it like nuclear fusion, right? There have been
people who've been excited about nuclear fusion a long time. If we ever get nuclear fusion,
it's going to be fantastic for the world. I'm glad people are working on it, but, you know,
it's not right around the corner. Those two reports, to me, say probably it'll be 2030 before
quantum computing is something that could happen. And when it does happen, you know, this is going
to be big science stuff. This is, you know, micro Kelvin, almost absolute zero things that if they
vibrate, if truck goes by, it won't work, right? So this will be in data center stuff. We're not
going to have a quantum cell phone. And it's probably a 2030 kind of thing. So I'm happy that
other people are working on it, but just, you know, it's hard with all the news about it,
not to think that it's right around the corner. And that's why we need to do something as Moore's
Law is slowing down to provide the computing, keep computing getting better for this next decade.
And, you know, we shouldn't be betting on quantum computing or expecting quantum computing to
deliver in the next few years. It's probably further off. You know, I'd be happy to be wrong.
It'd be great if quantum computing is going to commercially viable, but it will be a set of
applications. It's not a general purpose computation. So it's going to do some amazing
things, but there'll be a lot of things that probably, you know, the old fashioned computers
are going to keep doing better for quite a while. And there'll be a teenager 50 years from now
watching this video saying, look how silly David Patterson was saying. No, I just said,
I said 2030. I didn't say, I didn't say never. We're not going to have quantum cell phones.
So he's going to be watching it. Well, I mean, I think this is such a, you know,
given that we've had Moore's Law, I just, I feel comfortable trying to do projects
that are thinking about the next decade. I admire people who are trying to do things
that are 30 years out, but it's such a fast moving field. I just don't know how to,
I'm not good enough to figure out what, what's the problem is going to be in 30 years. You know,
10 years is hard enough for me. So maybe if it's possible to untangle your intuition a little bit,
I spoke with Jim Keller. I don't know if you're familiar with Jim. And he is trying to sort of
be a little bit rebellious and to try to think that. He quotes me as being wrong.
Yeah. So what are your, wait a minute, wait a minute for the record. Jim talks about that he
has an intuition that Moore's Law is not in fact, in fact, dead yet and that it may continue for
some time to come. What are your thoughts about Jim's ideas in this space? Yeah, this is just,
this is just marketing. So what Gordon Moore said is a quantitative prediction. We can check the
facts, right? Which is doubling the number of transistors every two years. So we can look back
at Intel for the last five years and ask him, let's look at DRAM chips six years ago. So that
would be three two year periods. So then our DRAM chips have eight times as many transistors as
they did six years ago. We can look up Intel microprocessors six years ago. If Moore's Law is
continuing, it should have eight times as many transistors as six years ago. The answer in both
those cases is no. The problem has been because Moore's Law was kind of genuinely embraced by
the semiconductor industry is they would make investments in similar equipment to make Moore's
Law come true. Semiconductor improving and Moore's Law in many people's minds are the same thing.
So when I say, and I'm factually correct that Moore's Law is no longer holds, we are not doubling
transistors every years years. The downside for a company like Intel is people think that means
it's stopped, that technology has no longer improved. And so Jim is trying to counteract
the impression that semiconductors are frozen in 2019 are never going to get better. So I never
said that. All I said was Moore's Law is no more. And I'm strictly looking at the number of transistors
because that's what Moore's Law is. There's been this aura associated with Moore's Law
that they've enjoyed for 50 years about look at the field we're in. We're doubling transistors
every two years. What an amazing field, which is an amazing thing that they were able to pull off.
But even as Gordon Moore said, no exponential can last forever. It lasted for 50 years, which is
amazing. And this is a huge impact on the industry because of these changes that we've been talking
about. So he claims, because he's trying to act, he claims Patterson says Moore's Law is no more,
and look at all, look at it, it's still going. And TSMC, they say it's no longer, but there's
quantitative evidence that Moore's Law is not continuing. So what I say now to try and, okay,
I understand the perception problem when I say Moore's Law is stopped. Okay, so now I say Moore's
Law is slowing down. And I think Jim, which is another way of, if he's, if it's predicting
every two years, and I say it's slowing down, then that's another way of saying it doesn't
hold anymore. And, and I think Jim wouldn't disagree that it's slowing down. Because that
sounds like it's, things are still getting better, just not as fast, which is another way of saying
Moore's Law isn't working anymore. It's still good for marketing. But, but what's your, you're
not, you don't like expanding the definition of Moore's Law sort of naturally.
You know, it's an educator, you know, are, you know, is this like modern politics? Is everybody
get their own facts? Or do we have, you know, Moore's Law was a crisp, you know, a more,
it was Carver Mead looked at his Moore's Conversations drawing on a log log scale,
a straight line. And that's what the definition of Moore's Law is. There's this other,
what Intel did for a while, interestingly, before Jim joined them, they said, oh, no,
Moore's Law isn't the number of doubling, isn't really doubling transistors every two years.
Moore's Law is the cost of the individual transistor going down, cutting in half every two
years. Now, that's not what he said, but they reinterpreted it. Because they believed that
the cost of transistors was continuing to drop, even if they couldn't get twice the main ships.
Yes. Many people in industry have told me that's not true anymore. That basically,
then in more recent technologies, they got more complicated, the actual cost of transistor went
up. So even, even the corollary might not be true. But certainly, you know, Moore's Law,
that was the beauty of Moore's Law. It was a very simple, it's like E equals MC squared, right?
It was like, wow, what an amazing prediction. It's so easy to understand the implications
are amazing. And that's why it was so famous as a prediction. And this reinterpretation of what
it meant and changing is, you know, his revisionist history. And I'd be happy. And they're not claiming
there's a new Moore's Law. They're not saying, by the way, it's, instead of every two years,
it's every three years. I don't think they want to say that. I think what's going to happen is
the new technology emissions, H1 is going to get a little bit slower. So it is slowing down.
The improvements won't be as great. And that's why we need to do new things.
Yeah, I don't like that the idea of Moore's Law is tied up with marketing. It would be nice if,
whether it's marketing or it's, well, it could be affecting business, but it could also be
affecting the imagination of engineers. If Intel employees actually believe that we're frozen in
2019, well, that's, that would be bad for Intel. They not just Intel, but everybody. It's inspired
Moore's Law is inspiring. Yeah, well, everybody. But what's happening right now, talking to people
who have working in national offices and stuff like that, a lot of the computer science community
is unaware that this is going on, that we are in an era that's going to need radical change at
lower levels that could affect the whole software stack. This, you know, if, if Intel, if you're
using cloud stuff and the servers that you get next year are basically only a little bit faster
than the servers you got this year, you need to know that. And we need to start innovating
to start delivering on it. If you're counting on your software, your software going to add a lot
more features, assuming the computers are going to get faster, that's not true. So are you going
to have to start making your software stack more efficient? Are you going to have to start learning
about machine learning? So it's, you know, it's kind of a, it's a warning or call for arms that
the world is changing right now. And a lot of people, a lot of computer science PhDs are unaware
of that. So a way to try and get their attention is to say that Moore's Law is slowing down and
that's going to affect your assumptions. And, you know, we're trying to get the word out. And
when companies like TSMC and Intel say, oh, no, no, no, Moore's Law is fine. Then people think,
okay, I don't have to change my behavior. I'll just get the next servers. And, you know,
if they start doing measurements, they'll realize what's going on.
It'd be nice to have some transparency and metrics for, for the lay person
to be able to know if computers are getting faster and not to forget. Yeah, there are,
there are a bunch of, most people kind of use clock rate as a, as a measure of performance.
You know, it's not a perfect one. But if you've noticed clock rates are more or less the same
as they were five years ago. Computers are a little better than they aren't. They haven't
made zero progress, but they've made small progress. So you, there's some indications
out there and then our behavior, right? Nobody buys the next laptop because it's so much faster
than the laptop from the past. For cell phones, I think, uh, I don't know why people buy new
cell phones, you know, because of the new ones announced, uh, the cameras are better, but that's
kind of domain specific, right? They're putting special purpose hardware to make the processing
of images go much better. So that's, that, that's the way they're doing it. They're not particularly,
it's not that the ARM processor in there is twice as fast as much as they've added accelerators
to help, uh, get the experience of the phone. Can we talk a little bit about one other exciting
space, arguably the same level of impact as, uh, your work with risk is raid. And in your,
in 1988, you co-authored a paper, a case for redundant arrays of inexpensive disks, hence
R-A-I-D raid. So that's where you introduced the idea of raid. Incredible that that little, I mean,
little, that paper kind of had this ripple effect and had a really revolutionary effect. So first,
what is raid? What is raid? So this is work I did with my colleague Randy Katz and a
star graduate student, Garth Gibson. So we had just done the fourth generation risk project
and, um, Randy Katz, which had an early, uh, Apple, uh, Macintosh computer. At this time,
everything was done with floppy disks, uh, which are, uh, old, uh, technologies, uh, that could
store things that didn't have much capacity. And you had to, to get any work done, you're
always sticking your little floppy disk in and out because they didn't have much capacity.
But they started building what are called hard disk drives, which is magnetic material that
can, uh, remember information storage for the Mac. And Randy asked the question when he saw this,
uh, disk, uh, next to his Mac. Gee, these are brand new small things. Before that, uh, for
the big computers that the disk would be the size of washing machines. And here's something,
the size of, uh, kind of the size of a book or so. I wonder what we could do with that. Well, we,
the, Randy was involved in the, in the fourth generation risk project here at Berkeley in the
80s. So we figured out a way how to make the computation part, the processor part go a lot
faster. But what about the storage part? The, uh, can we do something to make it faster? So
we hit upon the idea of taking a lot of these disks developed for personal computers at Macintosh's
and putting many of them together instead of one of these washing machine size things. And so
we worked the, wrote the first draft of the paper and we'd have 40 of these little PC disks
instead of one of these, uh, washing machine size things. And they would be much cheaper
because they're made for PCs and they could actually kind of be faster because there was 40
of them rather than one of them. And so we wrote a paper like that and send it to one of our former
Berkeley students at IBM. And he says, well, this is all great and good, but what about the
reliability of these things? Now you have 40 of these devices, each of which are kind of PC
quality. So they're not as good as these IBM washing machines. IBM dominated the, the, the,
the storage genesis. So the reliability is going to be awful. And so when we calculated it out,
instead of, you know, it breaking on average once a year, it would break every two weeks.
So we thought about the idea and said, well, we got to address the reliability. So we did it
originally performance, but we had to do reliability. So the name redundant array of inexpensive
disks is array of these disks, inexpensive life for PCs, but we have extra copies. So if one breaks,
we won't lose all the information. We'll have enough redundancy that we could let some break and
we can still preserve the information. So the name is an array of inexpensive disks. This is
a collection of these PCs and the R part of the name was the redundancy. So they'd be reliable.
And it turns out if you put a modest number of extra disks in one of these arrays, it could actually
not only be as faster and cheaper than one of these washing machine disks, it could be actually
more reliable because you could have a couple of breaks even with these cheap disks, whereas one
failure with the washing machine thing would, would knock it out. Did you, did you have a sense
just like with risk that in the 30 years that followed raid would take over as, as a, as a
mechanism for storage? I think it was, I'd say, I think I'm naturally an optimist, but I thought
our ideas were right. I thought kind of like Morris Law, it seemed to me, if you looked at the
history of the disk drives, they went from washing machine size things and they were getting smaller
and smaller and the volumes were with the smaller disk drives because that's where the PCs were.
So we thought that was a technological trend that disk drives, the volume of disk drives was going
to be small, getting smaller and smaller devices, which were true. They were the size of a, I don't
know, eight inches diameter than five inches than three inches in diameters. And so that it made sense
to figure out how to deal things with an array of disks. So I think it was one of those things
where logically we think the technological forces were on our side that it made sense. So we
expected it to catch on, but there was that same kind of business question. You know, IBM was the
big pusher of these disk drives in the real world where the technical advantage get turned into a
business advantage or not. It proved to be true. And so, you know, we thought we were sound technically
and it was unclear whether the business side, but we kind of, as academics, we believe that
technology should win and it did. And if you look at those 30 years, just from your perspective,
are there interesting developments in the space of storage that have happened in that time?
Yeah, the big thing that happened, or the couple of things that happened, what we did had a modest
amount of storage. So as redundancy, as people built bigger and bigger storage systems, they've
added more redundancy so they could add more failures. And the biggest thing that happened
in storage is for decades, it was based on things physically spinning called hard disk drives. We
used to turn on your computer and it would make a noise. What that noise was, was the disk drive
spinning and they were rotating at like 60 revolutions per second. And it's like, if you
remember the vinyl records, if you've ever seen those, that's what it looked like. And there was
like a needle like on a vinyl record that was reading it. So the big drive change is switching
that over to a semiconductor technology called flash. So within the last, I'd say about decade,
is increasing fraction of all the computers in the world are using semiconductor for storage,
the flash drive, instead of being magnetic, their optical, their semiconductor writing of
information into very densely. And that's been a huge difference. So all the cell phones in the
world use flash, most of the laptops use flash, all the embedded devices use flash instead of
storage. Still in the cloud, magnetic disks are more economical than flash, but they use both in
the cloud. So it's been a huge change in the storage industry, this the switching from primarily
disk to be primarily semiconductor. For the individual disk, but still the rate mechanism
applies to those different kinds of disk. Yes. The people will still use rate ideas, because
it's kind of what's different, kind of interesting kind of psychologically, if you think about it.
People have always worried about the reliability of computing since the earliest day. So kind of,
but if we're talking about computation, if your computer makes a mistake and
the computer says, the computer has ways to check and say, oh, we screwed up. We made a mistake.
What happens is that program that was running, you have to redo it, which is a hassle. For storage,
if you've sent important information away, and it loses that information, you go nuts.
This is the worst. Oh my God. So if you have a laptop and you're not backing it up
on the cloud or something like this, and your disk drive breaks, which it can do,
you'll lose all that information and you just go crazy. So the importance of reliability for
storage is tremendously higher than the importance of reliability for computation because of the
consequences of it. So yes, so rate ideas are still very popular, even with the switch of
the technology. Although flash drives are more reliable. If you're not doing anything like
backing it up to get some redundancy, so they handle it, you're taking great risks.
You said that for you and possibly for many others, teaching and research don't conflict
with each other as one might suspect. And in fact, they kind of complement each other. So
maybe a question I have is, how has teaching helped you in your research or just in your
entirety as a person who both teaches and does research and just thinks and creates new ideas
in this world? Yes. I think what happens is when you're a college student, you know there's this
kind of tenure system and doing research. So kind of this model that is popular in America,
I think America really made it happen, is we can attract these really great faculty to research
universities because they get to do research as well as teach. And that especially in fast
moving fields, this means people are up to date and they're teaching those kind of things.
But when you run into a really bad professor, a really bad teacher, I think the students think,
well, this guy must be a great researcher because why else could he be here? So as I,
you know, after 40 years at Berkeley, we had a retirement party and I got a chance to reflect
and I looked it back at some things. That is not my experience. There's a, I saw a photograph of
five of us in the department who won the Distinguished Teaching Award from campus,
a very high honor. You know, I've got one of those, one of the highest honors. So there are five of
us on that picture. There's Manuel Blum, Richard Karp, me, Randy Kast and John Osterhout,
contemporaries of mine. I mentioned Randy already. All of us are in the National Academy of Engineering.
We've all run the Distinguished Teaching Award. Blum, Karp and I all have touring awards.
Touring awards, right. You know, the highest award in computing. So that's the opposite,
right? It's what happens if you, it's, it's, they're highly correlated. So probably the other way
to think of it, if you're very successful people or maybe successful at everything they do,
it's not an either or. And it's an interesting question whether specifically, that's probably
true, but specifically for teaching, if there's something in teaching that it's the Richard
Feynman, right? Is there something about teaching that actually makes your research, makes you think
deeper and more outside the box and more insightful? Absolutely. Yeah. I was going to bring up Feynman.
I mean, he, he criticized the Institute of Advanced Studies. He, so the Institute of Advanced Studies
was this thing that was created near Princeton, where Einstein and all these smart people went.
And when he was invited, he, he thought it was a terrible idea. His, this is a university. It was,
it was supposed to be heaven, right? A university without any teaching. But he thought it was a
mistake is getting up in the classroom and having to explain things to students and having them ask
questions. Like, well, why is that true? Makes you stop and think. So he thinks, he thought,
and I agree, I think that interaction between a research university and having students
with bright young men's asking hard questions the whole time is, is synergistic. And, you know,
a university without teaching wouldn't be as vital and exciting a place. And I think it helps stimulate
the, the research. Another romanticized question, but what's your favorite concept or idea to teach?
What inspires you or you see inspire the students? Is there something that pops to mind or, or puts
the fear of God in them? I don't know, whichever is most effective. I mean, in general, I think
people are surprised. I've seen a lot of people who don't think they like teaching, come, come give
guest lectures or teach a course and get hooked on seeing the lights turn on, right? Is people,
you can explain something to people that they don't understand. And suddenly they get something,
you know, that's, that's not, that's important and difficult. And just seeing the lights turn on
is a, you know, it's a real satisfaction there. I don't think there's any specific example of
that. It's just the general joy of seeing them, seeing them understand. I have to talk about this
because I've wrestled. I do martial arts. Yeah. Of course, I love wrestling. I'm a huge, I'm Russian.
So I, oh, sure. I had to talk to Dan Gable on podcast. So I'm, Dan Gable is my era kind of guy.
So you've wrestled at UCLA among many other things you've done in your life,
competitively in sports and science and so on. You've, you've wrestled maybe, again,
continue with the romanticized questions, but what have you learned about life and maybe even
science from wrestling or from? Yeah, that's, in fact, I wrestled at UCLA, but also at El Camino
Community College. And just right now, we were in the state of California, we were state champions
at El Camino. And in fact, I was talking to my mom and I got into UCLA, but I decided to go to the
community college, which is, it's much harder to go to UCLA than the community college. And I asked,
why did I make the decision? Because I thought it was because of my girlfriend. She said,
well, it was the girlfriend and you thought the wrestling team was really good. And we were right.
We had a great wrestling team. We, we actually wrestled against UCLA at a tournament and we beat
UCLA as a community college, which is just freshmen and sophomores. And the part of reason I brought
this up is I'm going to go, they've invited me back at El Camino to give a lecture next month.
And so I'm, we've, my friend who was on the wrestling team and that we're still together,
we're right now reaching out to other members of the wrestling team. We can get together for a
union. But in terms of me, it was a huge difference. I was, I was both, I was kind of the age cut off.
I was, it was December 1st. And so I was almost always the youngest person in my class. And I
matured later on, you know, our family matured later. So I was almost always the smallest guy.
So, you know, I took, you know, kind of nerdy courses, but I was wrestling. So wrestling was
huge for my, you know, self-confidence in high school. And then, you know, I kind of got bigger
at El Camino and in college. And so I had this kind of physical self-confidence. And it's translated
into research self-confidence. And, and also kind of, I've had this feeling even today in my 70s,
you know, if something, if something going on in the streets that is bad physically,
I'm not going to ignore it, right? I'm going to stand up and try and straighten that out.
And that kind of confidence just carries through the entirety of your life.
Yeah. And the same things happens intellectually. If there's something going on where people are
saying something that's not true, I feel it's my job to stand up just like I would in the street.
If there's something going on, somebody attacking some woman or something, I'm not,
I'm not standing by and letting that get away. So I feel it's my job to stand up. So it's kind of
ironically translates. The other things that turned out, for both, I had really great college and
high school coaches, and they believed, even though wrestling's an individual sport,
that we'd be more successful as a team if we bonded together. You do things that we would
support each other, rather than everybody, you know, in wrestling, it's a one-on-one.
And you could be everybody's on their own, but he felt if we bonded as a team, we'd succeed.
So I kind of picked up those skills of how to form successful teams and how to, from wrestling.
And so I think one of, most people would say, one of my strengths is I can create teams of
faculty, large teams of faculty grad students, pull all together for a common goal and, you know,
and often be successful at it. But I got, I got both of those things from wrestling. Also,
I think I heard this line about if people are in kind of, you know, collision, you know, sports
with physical contact, like wrestling or football and stuff like that. People are a little bit more,
you know, assertive or something. And so I think, I think that also comes through as, you know,
in, I was, I didn't shy away from the risk-risk debates, you know, I was, I enjoyed taking on
the arguments and stuff like that. So it was, it was a, I'm really glad I did wrestling. I think
it was really good for my self-image and I learned a lot from it. So I think that's, you know, sports
done well, you know, there's really lots of positives you can take about it of leadership,
you know, how to, how to form teams and how, how to be successful.
So we've talked about metrics a lot. There's a really cool, in terms of bench press and weight
lifting, pound years metric that you've developed that we don't have time to talk about, but it's
a really cool that people should look into. It's rethinking the way we think about metrics and
weightlifting. But let me talk about metrics more broadly, since that appeals to you in all forms.
Let's look at the most ridiculous, the biggest question of the meaning of life.
If you were to try to put metrics on a life well-lived, what would those metrics be?
Yeah, a friend of mine, Randy Katz said this. He said, you know, when, when it's time to sign off,
it's, it's a, the measure isn't the number of zeros in your bank account. It's the number of inches
in the obituary in the New York times. Let's see, let's see, he said it. I think, you know, having,
and you know, this is a cliche is that people, people don't die wishing they'd spent more time
in the office, right? As I reflect upon my career, there've been, you know, a half a dozen or a
dozen things say I've been proud of. A lot of them aren't papers or scientific results. Certainly
my family, my wife, we've been married more than 50 years, kids and grandkids, that's really precious.
Education things I've done, I'm very proud of, you know, books and courses. I did some help with
underrepresented groups that was effective. So it was interesting to see what were the things
I reflected. You know, I had hundreds of papers, but some of them were the papers,
like the risk and rate stuff that I'm proud of, but a lot of them were, were not those things.
So people who are just spend their lives, you know, going after the dollars or going after
all the papers in the world, you know, that's probably not the things that are afterwards
you're going to care about. When I was, just when I got the offer from Berkeley before I showed up,
I read a book where they interviewed a lot of people and all works of life. And what I got out
of that book was the people who felt good about what they did was the people who affected people
as opposed to things that were more transitory. So I came into this job, assuming that it wasn't
going to be the papers, there's going to be relationships with the people over time that I
would, I would value. And that was a correct assessment, right? It's the people you work with,
the people you can influence, the people you can help is the things that you feel good about
towards your career. It's not, not the, the stuff that's more transitory.
I don't think there's a better way to end it than talking about your family,
the, the over 50 years of being married to your childhood sweetheart.
What I think I could add is how to, when you tell people you've been married 50 years,
they want to know why. How? Why? Yeah, I can tell you the nine magic words that you need to say
to your partner to keep a good relationship. And the nine magic words are, I was wrong.
You were right. I love you. Okay. And you got to say all nine. You can't say,
I was wrong. You were right. You're a jerk. You know, you can't say that. So yeah,
freely acknowledging that you made a mistake. The other person was right and that you love them
really gets over a lot of bumps in the road. So that's what I pass along.
Beautifully put, David, it is a huge honor. Thank you so much for the book you've written,
for the research you've done, for changing the world. Thank you for talking today.
Oh, thanks for the interview. Thanks for listening to this conversation with David Patterson.
And thank you to our sponsors, the Jordan Harbinger Show and Cash App. Please consider
supporting this podcast by going to jordanharbinger.com slash Lex and downloading Cash App and using
code Lex podcast. Click the links, buy the stuff. It's the best way to support this podcast and
the journey I'm on. If you enjoy this thing, subscribe on YouTube, review it with 5,000
podcasts, support it on Patreon or connect with me on Twitter. Alex Friedman spelled
without the E, try to figure out how to do that is just F R I D M A N. And now let me leave you
with some words from Henry David Thoreau. Our life is fiddled away by detail. Simplify, simplify.
Thank you for listening and hope to see you next time.