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      MIT Technology Review ©2020 v.|e^iπ|

  Moore's Law illustration
  MS Tech

[94]Computing / [95]Quantum Computing

We’re not prepared for the end of Moore’s Law

It has fueled prosperity of the last 50 years. But the end is now in sight.

  by [96]David Rotman
  Feb 24, 2020
  Moore's Law illustration
  MS Tech

  Gordon Moore’s 1965 forecast that the number of components on an
  integrated circuit would double every year until it reached an
  astonishing 65,000 by 1975 is the greatest technological prediction of
  the last half-century. When it proved correct in 1975, he revised what
  has become known as Moore’s Law to a doubling of transistors on a chip
  every two years.

  Since then, his prediction has defined the trajectory of technology
  and, in many ways, of progress itself.

  Moore’s argument was an economic one. Integrated circuits, with
  multiple transistors and other electronic devices interconnected with
  aluminum metal  lines on a tiny square of silicon wafer, had been
  invented a few years earlier by Robert Noyce at Fairchild
  Semiconductor. Moore, the company’s R&D director, realized, as he wrote
  in 1965, that with these new integrated circuits, “the cost per
  component is nearly inversely proportional to the number of
  components.” It was a beautiful bargain—in theory, the more transistors
  you added, the cheaper each one got. Moore also saw that there was
  plenty of room for engineering advances to increase the number of
  transistors you could affordably and reliably put on a chip.

  Soon these cheaper, more powerful chips would become what economists
  like to call a general purpose technology—one so fundamental that it
  spawns all sorts of other innovations and advances in multiple
  industries. A few years ago, leading economists credited the
  information technology made possible by integrated circuits with a
  third of US productivity growth since 1974. Almost every technology we
  care about, from smartphones to cheap laptops to GPS, is a direct
  reflection of Moore’s prediction. It has also fueled today’s
  breakthroughs in artificial intelligence and genetic medicine, by
  giving machine-learning techniques the ability to chew through massive
  amounts of data to find answers.

  But how did a simple prediction, based on extrapolating from a graph of
  the number of transistors by year—a graph that at the time had only a
  few data points—come to define a half-century of progress? In part, at
  least, because the semiconductor industry decided it would.
  Cover of Electronics Magazine April, 1965
  The April 1965 Electronics Magazine in which Moore's article appeared.
  Wikimedia

  Moore wrote that “cramming more components onto integrated circuits,”
  the title of his 1965 article, would “lead to such wonders as home
  computers—or at least terminals connected to a central
  computer—automatic controls for automobiles, and personal portable
  communications equipment.” In other words, stick to his road map of
  squeezing ever more transistors onto chips and it would lead you to the
  promised land. And for the following decades, a booming industry, the
  government, and armies of academic and industrial researchers poured
  money and time into upholding Moore’s Law, creating a self-fulfilling
  prophecy that kept progress on track with uncanny accuracy. Though the
  pace of progress has slipped in recent years, the most advanced chips
  today have nearly 50 billion transistors.

  Every year since 2001, MIT Technology Review has chosen the 10 most
  important breakthrough technologies of the year. It’s a list of
  technologies that, almost without exception, are possible only because
  of the computation advances described by Moore’s Law.

  For some of the items on this year’s list the connection is obvious:
  consumer devices, including watches and phones, infused with AI;
  climate-change attribution made possible by improved computer modeling
  and data gathered from worldwide atmospheric monitoring systems; and
  cheap, pint-size satellites. Others on the list, including quantum
  supremacy, molecules discovered using AI, and even anti-aging
  treatments and hyper-personalized drugs, are due largely to the
  computational power available to researchers.

  But what happens when Moore’s Law inevitably ends? Or what if, as some
  suspect, it has already died, and we are already running on the fumes
  of the greatest technology engine of our time?

RIP

  ^“It’s over. This year that became really clear,” says Charles
  Leiserson, a computer scientist at MIT and a pioneer of parallel
  computing, in which multiple calculations are performed simultaneously.
  The newest Intel fabrication plant, meant to build chips with minimum
  feature sizes of 10 nanometers, was much delayed, delivering chips in
  2019, five years after the previous generation of chips with
  14-nanometer features. Moore’s Law, Leiserson says, was always about
  the rate of progress, and “we’re no longer on that rate.” Numerous
  other prominent computer scientists have also declared Moore’s Law dead
  in recent years. In early 2019, the CEO of the large chipmaker Nvidia
  agreed.

  In truth, it’s been more a gradual decline than a sudden death. Over
  the decades, some, including Moore himself at times, fretted that they
  could see the end in sight, as it got harder to make smaller and
  smaller transistors. In 1999, an Intel researcher worried that the
  industry’s goal of making transistors smaller than 100 nanometers by
  2005 faced fundamental physical problems with “no known solutions,”
  like the quantum effects of electrons wandering where they shouldn’t
  be.

  For years the chip industry managed to evade these physical roadblocks.
  New transistor designs were introduced to better corral the electrons.
  New lithography methods using extreme ultraviolet radiation were
  invented when the wavelengths of visible light were too thick to
  precisely carve out silicon features of only a few tens of nanometers.
  But progress grew ever more expensive. Economists at Stanford and MIT
  have calculated that the research effort going into upholding Moore’s
  Law has risen by a factor of 18 since 1971.

  Likewise, the fabs that make the most advanced chips are becoming
  prohibitively pricey. The cost of a fab is rising at around 13% a year,
  and is expected to reach $16 billion or more by 2022. Not
  coincidentally, the number of companies with plans to make the next
  generation of chips has now shrunk to only three, down from eight in
  2010 and 25 in 2002.

  Finding successors to today’s silicon chips will take years of
  research.If you’re worried about what will replace moore’s Law, it’s
  time to panic.

  Nonetheless, Intel—one of those three chipmakers—isn’t expecting a
  funeral for Moore’s Law anytime soon. Jim Keller, who took over as
  Intel’s head of silicon engineering in 2018, is the man with the job of
  keeping it alive. He leads a team of some 8,000 hardware engineers and
  chip designers at Intel. When he joined the company, he says, many were
  anticipating the end of Moore’s Law. If they were right, he recalls
  thinking, “that’s a drag” and maybe he had made “a really bad career
  move.”

  But Keller found ample technical opportunities for advances. He points
  out that there are probably more than a hundred variables involved in
  keeping Moore’s Law going, each of which provides different benefits
  and faces its own limits. It means there are many ways to keep doubling
  the number of devices on a chip—innovations such as 3D architectures
  and new transistor designs.

  These days Keller sounds optimistic. He says he has been hearing about
  the end of Moore’s Law for his entire career. After a while, he
  “decided not to worry about it.” He says Intel is on pace for the next
  10 years, and he will happily do the math for you: 65 billion (number
  of transistors) times 32 (if chip density doubles every two years) is 2
  trillion transistors. “That’s a 30 times improvement in performance,”
  he says, adding that if software developers are clever, we could get
  chips that are a hundred times faster in 10 years.

  Still, even if Intel and the other remaining chipmakers can squeeze out
  a few more generations of even more advanced microchips, the days when
  you could reliably count on faster, cheaper chips every couple of years
  are clearly over. That doesn’t, however, mean the end of computational
  progress.

Time to panic

  Neil Thompson is an economist, but his office is at CSAIL, MIT’s
  sprawling AI and computer center, surrounded by roboticists and
  computer scientists, including his collaborator Leiserson. In a new
  paper, the two document ample room for improving computational
  performance through better software, algorithms, and specialized chip
  architecture.

  One opportunity is in slimming down so-called software bloat to wring
  the most out of existing chips. When chips could always be counted on
  to get faster and more powerful, programmers didn’t need to worry much
  about writing more efficient code. And they often failed to take full
  advantage of changes in hardware architecture, such as the multiple
  cores, or processors, seen in chips used today.

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  Thompson and his colleagues showed that they could get a
  computationally intensive calculation to run some 47 times faster just
  by switching from Python, a popular general-purpose programming
  language, to the more efficient C. That’s because C, while it requires
  more work from the programmer, greatly reduces the required number of
  operations, making a program run much faster. Further tailoring the
  code to take full advantage of a chip with 18 processing cores sped
  things up even more. In just 0.41 seconds, the researchers got a result
  that took seven hours with Python code.

  That sounds like good news for continuing progress, but Thompson
  worries it also signals the decline of computers as a general purpose
  technology. Rather than “lifting all boats,” as Moore’s Law has, by
  offering ever faster and cheaper chips that were universally available,
  advances in software and specialized architecture will now start to
  selectively target specific problems and business opportunities,
  favoring those with sufficient money and resources.

  Indeed, the move to chips designed for specific applications,
  particularly in AI, is well under way. Deep learning and other AI
  applications increasingly rely on graphics processing units (GPUs)
  adapted from gaming, which can handle parallel operations, while
  companies like Google, Microsoft, and Baidu are designing AI chips for
  their own particular needs. AI, particularly deep learning, has a huge
  appetite for computer power, and specialized chips can greatly speed up
  its performance, says Thompson.

  But the trade-off is that specialized chips are less versatile than
  traditional CPUs. Thompson is concerned that chips for more general
  computing are becoming a backwater, slowing “the overall pace of
  computer improvement,” as he writes in an upcoming paper, “The Decline
  of Computers as a General Purpose Technology.”

  At some point, says Erica Fuchs, a professor of engineering and public
  policy at Carnegie Mellon, those developing AI and other applications
  will miss the decreases in cost and increases in performance delivered
  by Moore’s Law. “Maybe in 10 years or 30 years—no one really knows
  when—you’re going to need a device with that additional computation
  power,” she says.

  The problem, says Fuchs, is that the successors to today’s general
  purpose chips are unknown and will take years of basic research and
  development to create. If you’re worried about what will replace
  Moore’s Law, she suggests, “the moment to panic is now.” There are, she
  says, “really smart people in AI who aren’t aware of the hardware
  constraints facing long-term advances in computing.” What’s more, she
  says, because application--specific chips are proving hugely
  profitable, there are few incentives to invest in new logic devices and
  ways of doing computing.

Wanted: A Marshall Plan for chips

  In 2018, Fuchs and her CMU colleagues Hassan Khan and David Hounshell
  wrote a paper tracing the history of Moore’s Law and identifying the
  changes behind today’s lack of the industry and government
  collaboration that fostered so much progress in earlier decades. They
  argued that “the splintering of the technology trajectories and the
  short-term private profitability of many of these new splinters” means
  we need to greatly boost public investment in finding the next great
  computer technologies.

  If economists are right, and much of the growth in the 1990s and early
  2000s was a result of microchips—and if, as some suggest, the sluggish
  productivity growth that began in the mid-2000s reflects the slowdown
  in computational progress—then, says Thompson, “it follows you should
  invest enormous amounts of money to find the successor technology.
  We’re not doing it. And it’s a public policy failure.”

  There’s no guarantee that such investments will pay off. Quantum
  computing, carbon nanotube transistors, even spintronics, are enticing
  possibilities—but none are obvious replacements for the promise that
  Gordon Moore first saw in a simple integrated circuit. We need the
  research investments now to find out, though. Because one prediction is
  pretty much certain to come true: we’re always going to want more
  computing power.
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