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C or em m om e iv at e su Is ®

Why the AI Revolution Is Only the Beginning: page 4 Intel’s Star Chip Architect on What Comes Next: page 17 Laying Down a Law: Gordon Moore’s Original Essay: page 20

Winter 2019 Free An Intel Publication

This commemorative magazine celebrates the 50th anniversary of Intel and the longevity of Moore’s Law. In tribute, we have designed it in the style of Electronics magazine in 1965, the year Gordon Moore’s famous original essay was published. 2

Table of Contents Why the AI Revolution Is Only the Beginning, by John Markoff................................................................ 4 We’re Living in the New Era of Computing, by Sam Madden.................................................................... 8 A Way Around Moore’s Law? Intel’s Chiplet Ace Explains, by Jonah Freedman.................................... 11 How Robots Got Better at Becoming Your Partner, by Anca Dragan...................................................... 14 Intel Chip Architect Jim Keller on What Comes Next, by Walden Kirsch................................................. 17 Laying Down a Law: The Essay That Started It All, by Gordon Moore.................................................... 20

Why Moore Is Still Our Guide When Intel co-founder Gordon Moore wrote his now-famous essay in 1965, he had a vision of a future where virtually every activity would interact with computing. That day has arrived. We’re at the starting line of a generation-defining digital transformation where a data-centric world is driving demand for all kinds of devices powered by computing, from cars to appliances to personal communication and everywhere in between. We celebrated Intel’s 50th anniversary in 2018, and we’re even more excited about what the future brings. But we wouldn’t be where we are today without Moore’s vision. That’s why I couldn’t be more pleased to welcome you to this commemorative magazine, which pays homage to his original essay, faithfully reprinted from the April 19, 1965 issue of Electronics. Your eyes don’t deceive you: This entire magazine is designed in that retro format. It’s a subtle nod to our past, and a celebration of how profoundly computing technology has changed the world over the last half-century. But this is no reboot. We’re again looking into the future. In the pages that follow, you’ll also read about some of the latest developments in artificial intelligence, robotics, sensor computing and more, all written by some of the most innovative minds in tech and academia today. Much like Moore, these are our visions of what is possible with data and technology, and how its applications will continue to change our world for the better. It’s proof positive that the next 50 years will be just as exciting as the last 50. I sincerely hope you enjoy this magazine as much as we enjoyed putting it together. And as for the journey ahead, we can’t wait to take it with you.

Dr. Murthy Renduchintala, Group President Technology, Systems Architecture & Client Group Chief Engineering Officer Intel Corporation


The Revolution Is Bigger Than You Know AI technology has already had a profound effect on chip-making, but there are bigger surprises on the way. By John Markoff

The artificial intelligence revolution isn’t coming—it’s already here. The dramatic success of AI has succeeded in transforming Silicon Valley over the past half-decade. Seemingly every shade of tech company—from startups all the way up to the dominant players—are pursuing big data, powerful pattern recognition algorithms and specialized silicon accelerators. What’s less clear is whether the AI renaissance is the future or simply a step on a path toward something even more radical. Several years ago, I attended a meeting of semiconductor industry partners at Stanford University. Much of the event was taken up with hand-wringing about the impending end of Moore’s Law. For decades, the industry designed exponentially more powerful computer processors and memories—and like clockwork, the advances made it possible for the creation of entire new industries with each successive microchip generation. Video game consoles, personal computers, MP3 players and smartphones all came in quick succession as Moore’s Law drove down the cost of silicon chips. Not only did processors get exponentially faster, their cost fell nearly just as fast. But as the chip makers approached fundamental atomic limits, there were increasing concerns that innovation would stall. Over dinner that night at Stanford, however, I met a young computer architect who was overjoyed by the end of what he thought of as a “free ride.” Enthusiastically, he mused, “Now it’s our turn!” In his view, it would soon be possible to explore radical new computer design ideas. Turned out that young man saw the future. It’s been more than 60 years since Cornell University psychologist Frank Rosenblatt unveiled the Perceptron, a single-layer neural network that was meant to be the beginnings of a computing device that would model the human brain. Newspaper accounts at the time reported that the Perceptron would soon lead to devices that would “walk, talk, see, write, reproduce itself and be conscious of their own existence.” In the years since Rosenblatt’s invention, there has been a metronome-like cycling back and forth between specialized computer hardware and general purpose processors. With the advent of the commercial microprocessor in the


AI capability has found its way into processors over the past decade. That in turn has changed how they’re engineered.


early 1970s, and the relentless cadence of Moore’s Law,

capitalize on their ability to blend powerful pattern recognition

special purpose hardware was soon made obsolete by the

systems, big data and inexpensive computing systems.

exponential improvements in computing speed and memory.

Put that through the lens of the impact of Deep Learning

Along the way, there have been repeated efforts to take

systems, powerful silicon-based pattern-recognition systems

ideas from the world of neuroscience and use them to inspire

that are transforming both the computing world as well the

computing designs. For example, in the mid-1980s, MIT

entire economy. In all, we’re experiencing a shift that foretells,

computer scientist Danny Hillis simulated the parallelism

at a minimum, the arrival of the era of intelligent machines—

found in the brain and designed the CM-1 supercomputer

and there’s possibly an even more radical transformation of

that was based on 65,536 1-bit processors. Several years

computing on the horizon.

later, Caltech physicist Carver Mead—who originally coined

Intel has, in many ways, been a bellwether for that

the phrase “Moore’s Law”—teamed up with Federico Faggin,

changing landscape, beginning with the strategic acquisition

one of the designers of Intel’s first microprocessor. Their firm

of reprogrammable chip-maker Altera in 2015 and continuing

was early to explore the potential of neural networks.

with a rapid succession of other AI acquisitions, such as

The slowing of Moore’s Law and the emergence of

Nervana™, Movidius, Mobileye and Vertex.AI. But the giant

powerful machine intelligence techniques has led to an

chipmaker is also running with a wave of other innovative tech

explosion of new design ideas as chip makers, computer

companies that are designing their own artificial intelligence

makers, car makers and even toy makers have moved to

accelerators, such as Amazon, Apple, Google, Microsoft

Neural engines have become key components of silicon design, which now can enable applications such as autonomous driving.

and Nvidia.

AI technology found its way into processors, dramatically

The scale of the impact of the silicon design renaissance

accelerating the speed of neural networks. And in many

can be seen in Apple’s recent addition of a “neural engine”

ways, AI is still in its early days with massive amounts of

to the microprocessor used to power the newest iPhone. As

infrastructure still in development.

I’ve previously reported, the newest version of the specialized

Since then, the practical applications of speech recognition

processor occupies only a small area on Apple’s chip, but

and computer vision applications have come to the fore as the

it has more equivalent computing power than the world’s

most advanced AI techniques have found their way into an

fastest supercomputer just two decades ago. That processor

ever-widening consumer market. That in turn has resulted in

in particular is a good example of how AI accelerators

a remarkable proliferation of new processor designs, making

are being used to drastically improve the performance of

possible—among other new markets—a world of increasingly

everyday consumer applications such as photo processing,

intelligent “things.”

video games, augmented reality and speech recognition. In

Where will this new wave of silicon end? I’ve been around

a similar fashion, Intel’s acquisition of Mobileye promises

Silicon Valley long enough to understand that there are

a wave of new silicon that will help make driving safer and

inevitably technology surprises around every corner. The

create a path toward self-driving vehicles.

new freedom realized by processor architects has raised

The pace of AI-focused design has accelerated

the possibility of even more radical departures in computer

significantly as the semiconductor industry has pushed ever

design and given new opportunities to those who were until

closer to atomic limits. It’s really only been since 2006 that

recently seen as heretics.


Researchers at MIT’s Center for Bits and Bots are working on machines that can assemble their own parts, and perhaps themselves.

Not long ago, I had breakfast with Neil Gershenfeld, a physicist who runs the Center for Bits and Atoms at MIT.

curve that has driven the semiconductor industry for the last half-century.

Gershenfeld is a deep critic of the modern field of computer

“Personal fabrication will mean that anybody will be able

science, but he believes now is the time to create a new

to make almost anything, anywhere,” Gershenfeld said

generation of intelligent machines that not only blur the

recently. “That will fundamentally change what is work, what

distinction between computing and storage, but “action” as

is education, what are supply chains and how we organize

well. Taking his inspiration not just from biology, but from


“reality,” he has begun to design computing devices that

If he’s right, the enthusiasm for AI currently sweeping

erase the line between computation and mechanical action.

Silicon Valley could be just a step on the way to something

Gershenfeld’s lab, working alongside NASA and the Pentagon, is designing silicon “assemblers” that may one day represent the future of both computing and manufacturing. Such next-generation manufacturing systems, he asserts, will completely change both worlds. If he’s right, the next computing wave may be far more profound than thinking machines. Several years ago, Gershenfeld sat with Intel co-founder Gordon Moore and showed him a chart that represented how his idea is on the same exponential

even more earth-shattering. John Markoff has written about technology and science since 1977. He was a reporter for The New York Times for 30 years and was part of a team that won a Pulitzer Prize for Explanatory Reporting in 2013 for its coverage of automation. He is currently a visiting scholar at the Center for Advanced Study in the Behavioral Sciences at Stanford University.

The views, assumptions and opinions expressed in this article are those of the author and do not necessarily reflect those of Intel.


We’re Living in the New Era of Computing A decade ago, the world wasn’t quite ready for sensor computing. That’s all about to change.

When I was a graduate student at the turn of the millennium—way back before smartphones and smart cars were a thing, and it was still considered cool to use Comic Sans in PowerPoint presentations—many computer scientists, including me, were captivated by the idea of what was called “sensor computing” or “sensor networking.” With the availability of low-power processors, wireless radios and a variety of sensors, it became possible to use networks of battery-powered devices to measure and sense the world. In my doctorate work, the applications of this technology included habitat monitoring on remote islands, measuring light and humidity in redwood forests, recording vibration on the Golden Gate Bridge and tracking vehicles as they drove through the desert. We built early prototypes of these systems, but ultimately we were limited by the hardware and networks of the time. Most deployed sensor systems could do little more than collect simple measurements like temperature, humidity

By Sam Madden

or occupancy of an environment a few times per minute. That limited the real-world impact of the technology, despite tremendous interest among academic circles. Modern computing devices, however, provide a lot more muscle toward these audacious goals—they’re dramatically more powerful, and can capture high-resolution, high-rate video and audio, as well as high-precision location data from GPS and a variety of other ranging and positioning technologies. These sensors, coupled with the ubiquity of highbandwidth wireless networks, energy-efficient processors on phones and cars, and new algorithms for making sense of this data, have sped up the ability to deliver on the promise of what sensor computing brought to the table. In short, the muscle we were waiting for finally showed up. In this new age of sensor computing, we’ll see computers even further integrated into our lives than they already are, and perhaps at a faster pace than we expected. Some of these shifts are already happening. For example, sensors and algorithms are enhancing safety features in cars to the point where truly autonomous automobiles are expected to arrive within the next few years. They’re also present in industrial applications, where a combination of high-resolution sensors and sophisticated algorithms can improve efficiency and optimize production. In healthcare, biotech and industrial engineering, companies like Novartis


Advances in sensor technology have accelerated to where truly autonomous cars are projected to be only a few years away.


and BASF are beginning to apply similar advanced imaging

On the software side, the way these new applications

techniques and advanced algorithms to synthesize and

make use of data is dramatically different than the

predict the properties of new compounds and materials.

simple processing we saw in sensing applications

There has been plenty written about how AI will change

a decade ago. In particular, there has been a boom in the

the modern world. Let’s look instead at the major shifts

availability of massive multimedia datasets and in software

that will power these changes. First, let’s consider the new

systems capable of storing this data. Those have allowed

algorithms for processing data—particularly the techniques

new, so-called “deep” neural networks: machine-learning

for perceiving patterns and objects in images and other

techniques that allow algorithms to excel at certain perceptual

rich sensor data. Second, take the rise of powerful sensing

tasks, such as recognizing objects, reading signs or

and “edge” computing devices in phones, cars and other

converting speech to text—in many cases, performing as

mobile devices. There is a shift towards more heterogeneous

well as or better than humans. These capabilities are critical

processing, which is being driven by the effectiveness of

in these new applications where we depend on computers to

hardware specialized to take on these perceptual tasks.

interact with humans in natural and humanistic ways, such as

These new technologies are complementary, and they drive a

driving cars, processing medical imagery or operating robotic

cycle of innovation similar to how the internet drove the rapid

equipment alongside humans.

development of server-side computing technology. That, in

On the hardware front, these new applications also

turn, enabled massive new software businesses built around

have driven a number of technological shifts, requiring new

ubiquitous connectivity to thrive. We’re about to experience

storage and low-power processing technologies for use

a similar effect now.

in edge devices, as well as a diversity and heterogeneity

Hardware, data and AI work in a complementary cycle where advancements in one area can help in the evolution of the other two.

of processing technology to power perceptual algorithms.

massive amounts of data and further the need for specialized

Consider autonomous vehicles: There may be several

hardware. And that in turn makes the algorithms better.

general-purpose CPUs on a car, responsible for overall

This cycle of technology behind this new era of computing

planning and coordination of processing. There may also

applications help makes our world a safer, more connected

be a number of specialized processors—such as GPUs,

place. It creates exciting new businesses and alters the way

Google’s tensor processing unit (TPU) or Intel’s Nervana—

many traditional enterprises—from hospitals to car makers

for very efficient evaluation of deep nets to process video

to insurance companies—operate. These changes also bring

and audio to identify objects, recognize scenes or interpret

to fruition the vision of sensor computing that inspired me as

speech. Server-side technology used to coordinate and

a young graduate student. Even if I had to learn on my own

share data between vehicles will in turn use a variety of

that Comic Sans wasn’t, in fact, all that cool.

processing technologies, including high-power serverclass processors as well as more advanced versions of the specialized processors to run the perceptual algorithms on the cars themselves. This new hardware, coupled with data processing and collection techniques, and perceptual algorithms, are locked in a virtuous cycle. New deep perceptual algorithms work because of the availability of both massive amounts of data and powerful specialized hardware. These algorithms enable new applications—such as autonomous driving and personalized healthcare—that produce even more

Samuel Madden is a professor of electrical engineering and computer science at MIT’s Computer Science and Artificial Intelligence Laboratory. He is also a founder of several companies, including Vertica, a next-generation analytical relational database system, and Cambridge Mobile Telematics, which develops technology to make roads safer by making drivers better. His research interests include databases, networking and sensor computing.

The views, assumptions and opinions expressed in this article are those of the author and do not necessarily reflect those of Intel.


You May Say I’m a Dreamer

Ramune Nagisetty is out to prove there’s more to Moore’s Law. As senior principal engineer in Intel’s Technology and Innovation office, she started leading Intel’s vision on chiplets eight years ago. The timing was no coincidence, as there has been an explosion in different types of computing: wearables, mobile, ambient computing, PC, the cloud and so on. In short, the days of the “one or two sizes fit all” approach are coming to an end. Nagisetty and her team are finding ways to

Intel’s lead on chiplets thinks Moore got it right—even if he couldn’t picture what the future looked like.

address that changing nature of computing by using chiplets and package integration. In other words, instead of shrinking a single chip indefinitely—the central concept of Moore’s Law— chiplets are used for heterogeneous integration, to mix and match different functions to meet a wider variety of product requirements. The implications are, as Nagisetty says, “huge, because it changes how we conduct business and how we create products.”

By Jonah Freedman

She would know all about that. Nagisetty has been at Intel for more than 20 years across multiple workflows, many of which touch directly on silicon. But she’s also been mapping the future, not just in her strategy roles, but also in the advancement of women in the tech industry. We caught up with her to talk about some of those future visions, including why chiplets are a path forward, what other developments are on the horizon and what advice she’d give young women aspiring to a career in engineering.

When Gordon Moore wrote his original essay in 1965, do you think he foresaw something like chiplets, which is almost a way around what we now call Moore’s Law? I don’t think Gordon Moore was ever expecting to create a “law,” and he certainly didn’t expect his paper would have a longevity of more than 50 years. But there are a few sentences in it where he actually predicted this direction we’re going in now. He didn’t use exactly the same words that we use today, but he did write, “It may prove to be more economical to build large systems out of smaller functions, which are separately packaged and interconnected. … The availability of large functions would allow the manufacturer large systems to design and construct a variety of equipment, both rapidly and economically.” Essentially, this is what we’re doing with chiplets. So, yes, I would say this is an evolution of Moore’s Law.


It sounds like you’re suggesting he indeed

monitor a healing wound or measure blood oxygenation while

almost envisioned technology like chiplets as

a person is exercising. Our health monitoring is very narrow

a game-changer.

right now, and there’s a next generation of developments in

Somewhat. Ironically, I think science fiction authors

wearables that will teach us a lot about human physiology

are better at predicting the future than technologists and

and how varied it is from person to person and in different

engineers. I remember some quote from way back when

types of conditions.

people weren’t really sure what the practical use of a PC would be, and someone speculated they might be used for

Let’s shift to women in technology. There are more

collecting recipes for cooking. I remember even being in a

women in prominent positions in the industry these

meeting where people were asking, “Why would anyone

days, but that’s been a big shift from the beginnings

want a camera in a phone?” As technologists, we’re always

of Silicon Valley. You made your way up the ladder

thinking in terms of constraint. Picturing the future often

through two-plus decades in tech. What has your

requires the imagination of people who are outside the

experience been like?

industry who don’t think in any constrained kind of way. So

I’ve encountered a lot of hurdles, but I don’t necessarily

in many ways, the dreamers have been better at predicting

always attribute it to being a woman. I have a natural

the future than the actual technologists.

inclination to work against the boundaries of the status quo, and being a woman is just a part of that. Things have improved, but honestly, Intel has given me tons of opportunities for both technical and personal development.

As technologists, we’re always thinking in terms of constraint. Picturing the future often requires the imagination of people who are outside the industry.

In my 23 years here, I would say I’ve had at least three, if not four careers, and I think that speaks a lot to how interesting it is to work in tech. That said, I know I’ve been fortunate. So part of my work has also been in mentorship activities, and a lot has changed there, too. In 2006, we started the Women’s Principal Engineers Forum. Today, it’s called the Women’s Principal Engineers and Fellows Forum because now we actually have women Fellows. So there have certainly been strides. One of the things we’ll face in the future is being able to find the highly talented workforce that a company like Intel relies on. And if we’re only tapping into half the adult working population, we’re going to come up short. So this is turning

Speaking of the future: Besides chiplets, what are some other emerging technologies you’re really

into not just the right thing to do, but a business imperative as well.

excited about? I’m still really interested in wearable computing, which

Right, as you mentioned, you’ve been involved in

I spent five years working on. There will be a time when

a lot of professional organizations for women in tech,

everyone will have adopted wearable devices, and I think it’s

including the Society of Women Engineers. Is there

going to be the really big breakthrough in understanding how

a collective sense the playing field is beginning to

we live, how we grow and how our health changes. That’s

even out?

something that will potentially take a while, but I think it’s just

The biggest indicator I have that things have shifted is

a matter of time. I was working with university researchers

comparing my experience to my mother’s. She was an

to create flexible, wearable sensors that could be integrated

engineer at Ford Motor Company and had master’s degrees

into smart Band-Aids, which could be used to, for example,

in physics, electrical engineering and computer science,


Nagisetty began her career at Intel in 1995 as a transistor engineer. Today, she leads the company’s vision around chiplet technology.

yet she still faced really formidable challenges. At that time, there was very little awareness of any kind of issue and there was really no desire to change. In just one generation, my experience and hers have been totally different. My generation has benefited from women like my mother who led the way and had a much harder time. I think the trend will continue, and I think the next generation will benefit from what’s going on right now. Part of that is the change in awareness and the desire for change. Industry leaders—both

I don’t think enough people recognize engineering as a creative endeavor. Part of it is imagining the future and part of it is creating the future.

at Intel and beyond—have talked about a desire for change. It’s out in the open and it’s being talked about everywhere.


You mentioned your mentorship activities.

very creative and they don’t look at engineering that way.

What advice do you give to young women who are

That’s a common misperception that has to stop. The simple

interested in pursuing engineering and competing?

fact is you can work across disciplines, such as biology

I always encourage young women and minorities to

and neuroscience. Computing is now really woven into the

pursue engineering, not just because there are so many

fabric of our lives, and it will only become even more so. It’s

opportunities, but also because your career will really change

a discipline that looks across those adjacent disciplines,

over time and you won’t get bored. I don’t think enough

and that’s really a powerful way to inform the work and the

people recognize engineering as a creative endeavor. Part

direction. That’s also where the most interesting opportunities

of it is imagining the future and part of it is creating the future.

are. Engineering might be one of the most creative career

I think women and girls are often attracted to things that seem

paths anyone can embark on.

My Collaborator Is a Robot

Back in the 1970s, Freddy the Robot was the celebrity in residence at the University of Edinburgh’s Department of Artificial Intelligence. A look at an early demo shows him hard at work: A scientist drops a bag of parts in front of him. Freddy then identifies the jumbled-up parts via computer vision and starts using his hand to unscramble them, pick them up one by one and assemble them into a “product.” Amazingly, what emerges is a shiny, yellow toy car. Fifty years later, I’m in awe of Freddy, completely humbled

Modern machines are better at perception and interaction, thanks in part to Moore’s Law.

by the scientists and engineers who put him together. In my lab at UC Berkeley, I stare at the robot arm I have (we call it “Archie”), and think back to the months it took us to write its planner (the decision-making engine that powers its behavior), controller (the system that decides how to spin its motors to make the planned behavior a reality) and perception system (Archie’s “eyes,” which enable it to figure

By Anca Dragan

out what objects are in front of it), so it can pretty much do the same thing Freddy was already doing so well back then. So what’s changed? When I take a closer look at our system, it’s doing something remarkably similar to what Freddy did. But if I put a coffee mug in front of Archie, he easily figures out it’s a mug. The same goes for my watch. These are more weirdly shaped objects, but computer vision has come a long way since those early days. Rather than relying on rules or looking for a specific color against a different background, today’s vision systems extract their own ways of detecting objects by finding patterns in datasets of millions of images. That it’s possible to process all these images is a testament to the new computing power we’ve gained. That a machine can identify useful patterns is to some extent due to new algorithms, but to a large extent it’s also enabled by our ability to quickly iterate on these algorithms by trying them out and seeing what they come up with. What used to be a large-scale experiment even in the 1990s is today what equates to our students’ coffee break. Even better, Archie doesn’t just move objects around for only one task, but can move objects between arbitrary poses while avoiding collisions. This used to be very hard because arms tend to have seven degrees of freedom, so they have to reason in not three or four, but seven dimensions. The problem becomes exponentially harder with the number of dimensions, and only in the ’90s did we finally create algorithms powerful enough to do battle with this kind of


Because it can process physical prompts, “Archie” can infer it’s holding a coffee cup too high above the ground for human comfort.

complexity and win. When I started graduate school in 2009, even with this

will be difficult for me when I’m older.

significant progress, I’d still have to wait three to four seconds

Archie uses extra compute cycles to make predictions of

for a robot arm to figure out how to move a bottle from A

where my arm will move next, and to be sure to stay out my

to B. And even then, it would come up with some totally

way. He becomes more careful if I start acting in a weird way

inefficient, unnatural solution—the arm would go side to side

that doesn’t fit with his predictive model of me. He sometimes

for no apparent reason, snaking its way through a maze that

guides me in what to put where as we sort out the objects.

wasn’t really there. Now, Archie ponders for milliseconds and

He can also sacrifice some efficiency here and there to

figures it out. What’s more, he moves efficiently, with no more

become more deliberate and transparent about what he’s

snaking back and forth, a little more like a human arm. I’d like

doing, slowing down and exaggerating his motion by using

to believe that those of us who worked on randomized and

a simple mental model of me and what I can see and predict.

trajectory optimization-based motion planning get the credit,

That way, he makes sure I’m able to understand what he’s up

but 10 years of Moore’s Law sure helped us.

to and can make some room for him. And he lets me know

Because Archie moves a little more naturally, I have an easy time being close to him, which means that at times,


interesting tasks—Archie might be able to assist with what

when something is too heavy for his rather flimsy motors and when he needs my help.

Archie and I can actually work together. For now, our

Next, my student Andrea walks into the lab. She sees

collaboration is limited to clearing up the table and sorting

Archie carrying her coffee mug, and she’s unhappy he’s

stuff into bins. But in the future, hopefully we’ll tackle more

holding it so high above the table—if he drops it, it will break.

In her research in robotics, Anca Dragan puts a heavy emphasis on ensuring robots and people can work together.

She pushes Archie’s arm down. Instead of him popping

on a track in a matter of days, for instance. However, you

right back up, Archie infers he must have been doing the

can’t easily get that same car to properly negotiate merging

task wrong, immediately replanning his movement to go

onto the highway in heavy traffic with other people. And

lower. The robots of 50 years ago were were rigid in both

highly dexterous manipulation in unstructured environments

their control, as well as the rules they would follow. Archie is

remains something that requires a lot of task-specific training

compliant and adapts to human input, as well as what this

or engineering.

input communicates about the rules he should follow and objectives he should achieve.

Overall, we’re making progress towards robots becoming more and more capable at doing the things we tell them to,

Archie stands on the shoulders of not just Freddy, but

but we’re not quite there yet. When we do get there, new

the work of every robotics researcher who has come since,

research challenges will emerge, like figuring out what we

and the work of those behind Moore’s Law. Together, they

should tell them to do in the first place!

enabled advances in perception, planning and optimal control, handling uncertainty, learning from prior experiences, and the ability to collaborate with and learn from people. We’ve come a long way from the days of Freddy the Robot, but we still have a long way to go. Some things that used to be entire research agendas are now commodities: You can set up an autonomous car to drive

Anca Dragan is an assistant professor of robotics at UC Berkeley and runs the InterACT Lab, where she focuses on algorithms for human-robot interaction. She is also on the leadership committees for the Berkeley Artificial Intelligence Research Lab and the Center for HumanCompatible AI.

The views, assumptions and opinions expressed in this article are those of the author and do not necessarily reflect those of Intel.


Transformation Is on the Way Intel’s chief chip architect offers his take on unlocking new innovation.

When Intel announced in April 2018 it had hired Jim Keller as a senior VP to lead its silicon engineering work, it wasn’t small news in the tech world. AnandTech’s headline called Keller a “CPU design guru.” VentureBeat went one further, calling Keller a “rock star chip architect.” Keller is now the general manager of Intel’s Silicon Engineering Group. Why did Intel hire him? As chief engineering officer Murthy Renduchintala wrote at the time, “Jim is one of the most respected microarchitecture design visionaries in the industry,” who will help Intel speed our work to “fundamentally change the way we build silicon.” After a series of senior leadership roles in both the x86 and

By Walden Kirsch

ARM worlds, why did Keller come to Intel? And why now? That’s where we started our recent conversation.

How important is Intel’s IP? Our crown jewels are our engineers. They’ve created lots of valuable technical assets, but every year it changes. IP depreciates pretty fast. So you have to keep reinventing and reinvigorating it. To put different kinds of IP together successfully takes a lot of work. That’s a hard problem to solve, and the industry has been working on it for a while. I think Intel started doing that later than some others. But that transformation is well on the way. What we have to do is get great at it. I think that’s a technical change, not a culture change.

From your perspective, what does Intel need to do differently? First I have to say, everywhere I go at Intel, I meet really great people. We have technical expertise, talent and energy everywhere I go. Comparing yourself to yourself, you get in your own bubble that way. There’s places where we are the best thing that’s ever happened, and maybe we’re even doing the best possible, but we have to make sure we’re super clear about that. We also need to continue to prioritize sustainable innovation. That’s something Intel has done and will continue to do in the future and something for which we can be counted on. Other companies have peaks and inflections they hit, but they struggle to repeat them. Intel has the know-how, talent and track record to do this, which is something that will enable us to take our leadership into the future, which will be a new era of computing innovation.


Jim Keller joined Intel in April 2018 as senior VP in charge of silicon engineering after spending time innovating for Tesla, AMD and Apple.

believe in focus. One of Steve Jobs’ famous quotes was,

When you joined, Murthy said that one of the reasons Intel was delighted to have you is that it’s entering a world of heterogeneous processes and architectures. Help us understand what that means.

“Why screw up two things when you can only get one thing

The pillars of our business today are PCs and servers. We

How was your experience at Apple? That was weirdly fun. Apple was a very dynamic place. It was very much a leadership-focused company. It was very aggressive. They really reach for things, but they also really


tend to build PCs around a processor, and then we leverage

There’s a lot of trying to figure out what the market needs,

that processor in servers, and then we build servers of very

and what’s the best tradeoff between performance and cost,

dense processors and memory systems. Heterogeneous

as opposed to what the best thing is we could possibly do. The interesting thing about the best you can do is that helps you push the envelope.

So what does a Jim Keller goal look like in general? I like to set a goal to be clear and simple. You might have 100 great people and 100 opinions. That’s a lot of stuff, and boiling it down to a simple goal is not easy. Having clear goals is not an easy thing, but it’s the way you do great products.

In the future, we may build products on different pieces of silicon and pull them together into a heterogeneous system. The complexity of that, as you build each kind of computing system, goes up. 18

computing means that now we have CPUs for running games and some graphics. We have media codecs for doing video encoding and decoding. So that’s a different kind of computing. Now we’re building AI computing devices, and they’re different too. So the range of computing types is expanding. In the future, we may build products on different pieces of silicon and pull them together into a heterogeneous system. The complexity of that, as you build each kind of computing system, goes up. Somehow or other, Moore’s Law just keeps

That whole time, right? But somehow or other, it just keeps

on going. So I’ve decided not to worry about it for the rest

on going. Moore’s Law is essentially an assertion that we

of my life.

will continue to shrink technology and raise performance.

We’d be remiss if we didn’t ask you about Moore’s Law. How do you look at Moore’s Law, and specifically what do you see as changing and what’s not?


So the range of computing types is expanding. In the future, we may build products on different pieces of silicon and pull them together into a heterogeneous system.

At different junctures, different things had to happen from a manufacturing perspective. And Intel has participated as a leader in many of those inventions. So go ahead, think about Moore’s Law. It’ll continue. The press will write about the end of it over and over. And in 10 years we’ll be thinking, boy, that

I’ve been working on computers for almost 40 years.

was really hard what we did. Now it’s going to be “over” again.

Everybody has always been predicting Moore’s Law will

That’s been the truth for a long time. So I’m not that worried

end in 10 to 15 years, which is reduced to five to 10 years.

about Moore’s Law.

How It All Began

Laying Down a Law In 1965, Intel’s founding father made a prediction that defined how chips would be manufactured for the next half-century. Here’s his original essay. By Gordon Moore, Intel Co-Founder This article originally appeared in Electronics, Volume 38, Number 8, April 19, 1965

The future of integrated electronics is the future of

today as well as any additional ones that result in electronics

electronics itself. The advantages of integration will bring

functions supplied to the user as irreducible units. These

about a proliferation of electronics, pushing this science into

technologies were first investigated in the late 1950s. The

many new areas.

object was to miniaturize electronics equipment to include

Integrated circuits will lead to such wonders as home

increasingly complex electronic functions in limited space

computers or at least terminals connected to a central

with minimum weight. Several approaches evolved, including

computerautomatic controls for automobiles, and personal

microassembly techniques for individual components, thinfilm

portable communications equipment. The electronic

structures and semiconductor integrated circuits.

wristwatch needs only a display to be feasible today.

Each approach evolved rapidly and converged so that

But the biggest potential lies in the production of large

each borrowed techniques from another. Many researchers

systems. In telephone communications, integrated circuits in

believe the way of the future to be a combination of the

digital filters will separate channels on multiplex equipment.

various approaches.

Integrated circuits will also switch telephone circuits and perform data processing.

The advocates of semiconductor integrated circuitry are already using the improved characteristics of thin-

Computers will be more powerful, and will be organized

film resistors by applying such films directly to an active

in completely different ways. For example, memories built

semiconductor substrate. Those advocating a technology

of integrated electronics may be distributed throughout the

based upon films are developing sophisticated techniques

machine instead of being concentrated in a central unit. In

for the attachment of active semiconductor devices to the

addition, the improved reliability made possible by integrated

passive film arrays.

circuits will allow the construction of larger processing units. Machines similar to those in existence today will be built at lower costs and with faster turn-around.

Present and Future

Both approaches have worked well and are being used in equipment today.

The Establishment Integrated electronics is established today. Its techniques

By integrated electronics, I mean all the various

are almost mandatory for new military systems, since the

technologies which are referred to as microelectronics

reliability, size and weight required by some of them is


achievable only with integration. Such programs as Apollo,

functions. But silicon will predominate at lower frequencies

for manned moon flight, have demonstrated the reliability

because of the technology which has already evolved around

of integrated electronics by showing that complete circuit

it and its oxide, and because it is an abundant and relatively

functions are as free from failure as the best individual

inexpensive starting material.

transistors. Most companies in the commercial computer field have machines in design or in early production employing

Reduced cost is one of the big attractions of integrated

integrated electronics. These machines cost less and perform

electronics, and the cost advantage continues to increase as

better than those which use conventional electronics.

the technology evolves toward the production of larger and

Instruments of various sorts, especially the rapidly

larger circuit functions on a single semiconductor substrate.

increasing numbers employing digital techniques, are starting

For simple circuits, the cost per component is nearly

to use integration because it cuts costs of both manufacture

inversely proportional to the number of components, the

and design.

result of the equivalent piece of semiconductor in the

The use of linear integrated circuitry is still restricted

equivalent package containing more components. But

primarily to the military. Such integrated functions are

as components are added, decreased yields more than

expensive and not available in the variety required to satisfy

compensate for the increased complexity, tending to raise

a major fraction of linear electronics. But the first applications

the cost per component. Thus there is a minimum cost at any

are beginning to appear in commercial electronics, particularly

given time in the evolution of the technology. At present, it is

in equipment which needs low-frequency amplifiers of

reached when 50 components are used per circuit. But the

small size.

Reliability Counts

minimum is rising rapidly while the entire cost curve is falling (see graph below). If we look ahead five years, a plot of costs suggests that the minimum cost per component might be

In almost every case, integrated electronics has

expected in circuits with about 1,000 components per circuit

demonstrated high reliability. Even at the present level of

(providing such circuit functions can be produced in moderate

production low compared to that of discrete componentsit

quantities.) In 1970, the manufacturing cost per component

offers reduced systems cost, and in many systems improved

can be expected to be only a tenth of the present cost.

performance has been realized. Integrated electronics will make electronic techniques more generally available throughout all of society, performing many functions that presently are done inadequately by other techniques or not done at all. The principal advantages will be lower costs and greatly simplified design payoffs from a ready supply of low-cost functional packages. For most applications, semiconductor integrated circuits will predominate. Semiconductor devices are the only reasonable candidates presently in existence for the active elements of integrated circuits. Passive semiconductor elements look attractive too, because of their potential for low cost and high reliability, but they can be used only if precision is not a prime requisite. Silicon is likely to remain the basic material, although others will be of use in specific applications. For example, gallium arsenide will be important in integrated microwave


Costs and Curves

The complexity for minimum component costs has

integrated circuits are already underway using multilayer

increased at a rate of roughly a factor of two per year (see

metalization patterns separated by dielectric films. Such a

graph on next page). Certainly over the short term this rate

density of components can be achieved by present optical

can be expected to continue, if not to increase. Over the

techniques and does not require the more exotic techniques,

longer term, the rate of increase is a bit more uncertain,

such as electron beam operations, which are being studied

although there is no reason to believe it will not remain

to make even smaller structures.

nearly constant for at least 10 years. That means by 1975, the number of components per integrated circuit for minimum cost will be 65,000. I believe that such a large circuit can be built on a single wafer.

Two-mil Squares

Increasing the Yield There is no fundamental obstacle to achieving device yields of 100%. At present, packaging costs so far exceed the cost of the semiconductor structure itself that there is no incentive to improve yields, but they can be raised as high as is economically justified. No barrier exists comparable to

With the dimensional tolerances already being employed

the thermodynamic equilibrium considerations that often limit

in integrated circuits, isolated high-performance transistors

yields in chemical reactions; it is not even necessary to do

can be built on centers two thousandths of an inch apart.

any fundamental research or to replace present processes

Such a two-mil square can also contain several kilohms

Only the engineering effort is needed.

of resistance or a few diodes. This allows at least 500

In the early days of integrated circuitry, when yields were

components per linear inch or a quarter million per square

extremely low, there was such incentive. Today ordinary

inch. Thus, 65,000 components need occupy only about

integrated circuits are made with yields comparable with

one-fourth a square inch.

those obtained for individual semiconductor devices. The

On the silicon wafer currently used, usually an inch or more in diameter, there is ample room for such a structure if the components can be closely packed with no space wasted for interconnection patterns. This is realistic, since efforts to achieve a level of complexity above the presently available

same pattern will make larger arrays economical, if other considerations make such arrays desirable.

Heat Problem Will it be possible to remove the heat generated by tens of


thousands of components in a single silicon chip?

interconnected. The availability of large functions, combined

If we could shrink the volume of a standard high-speed

with functional design and construction, should allow the

digital computer to that required for the components

manufacturer of large systems to design and construct

themselves, we would expect it to glow brightly with present

a considerable variety of equipment both rapidly and

power dissipation. But it won’t happen with integrated circuits.


Since integrated electronic structures are two-dimensional, they have a surface available for cooling close to each center

Linear Circuitry

of heat generation. In addition, power is needed primarily to

Integration will not change linear systems as radically as

drive the various lines and capacitances associated with the

digital systems. Still, a considerable degree of integration

system. As long as a function is confined to a small area on

will be achieved with linear circuits. The lack of large-

a wafer, the amount of capacitance which must be driven

value capacitors and inductors is the greatest fundamental

is distinctly limited. In fact, shrinking dimensions on an

limitations to integrated electronics in the linear area.

integrated structure makes it possible to operate the structure at higher speed for the same power per unit area.

Day of Reckoning

By their very nature, such elements require the storage of energy in a volume. For high Q it is necessary that the volume be large. The incompatibility of large volume and integrated electronics is obvious from the terms themselves.

Clearly, we will be able to build such component crammed

Certain resonance phenomena, such as those in

equipment. Next, we ask under what circumstances we

piezoelectric crystals, can be expected to have some

should do it. The total cost of making a particular system

applications for tuning functions, but inductors and capacitors

function must be minimized. To do so, we could amortize the

will be with us for some time.

engineering over several identical items, or evolve flexible

The integrated r-f amplifier of the future might well consist

techniques for the engineering of large functions so that

of integrated stages of gain, giving high performance at

no disproportionate expense need be borne by a particular

minimum cost, interspersed with relatively large tuning

array. Perhaps newly devised design automation procedures


could translate from logic diagram to technological realization without any special engineering.

Other linear functions will be changed considerably. The matching and tracking of similar components in integrated

It may prove to be more economical to build large systems

structures will allow the design of differential amplifiers of

out of smaller functions, which are separately packaged and

greatly improved performance. The use of thermal feedback effects to stabilize integrated structures to a small fraction of a degree will allow the construction of oscillators with crystal stability. Even in the microwave area, structures included in the definition of integrated electronics will become increasingly important. The ability to make and assemble components small compared with the wavelengths involved will allow the use of lumped parameter design, at least at the lower frequencies. It is difficult to predict at the present time just how extensive the invasion of the microwave area by integrated electronics will be. The successful realization of such items as phased-array antennas, for example, using a multiplicity of integrated microwave power sources, could completely revolutionize radar.


This magazine includes forward-looking statements relating to Intel. All statements that are not historical facts are subject to a number of risks and uncertainties, and actual results may differ materially. Please refer to Intel’s most recent earnings release, 10-Q and 10-K filings for the risk factors that could cause actual results to differ.


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Electronics Magazine  

This commemorative magazine celebrates the 50th anniversary of Intel and the longevity of Moore’s Law. In tribute, we have designed it in th...

Electronics Magazine  

This commemorative magazine celebrates the 50th anniversary of Intel and the longevity of Moore’s Law. In tribute, we have designed it in th...


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