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Nanoelectronics the Original Positronic Brain?

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Nanoelectronics the Original Positronic Brain? Nanoelectronics the Original Positronic Brain? Dan Hammerstrom Department of Electrical and Computer Engineering Portland State University Maseeh College of Engineering 12/13/08 1 and Computer Science Wikipedia: “A positronic brain is a fictional technological device, originally conceived by science fiction writer Isaac Asimov “Its role is to serve as a central computer for a robot, and, in some unspecified way, to provide it with a form of consciousness recognizable to humans” How close are we? You can judge the algorithms, in this talk I will focus on hardware and what the future might hold Maseeh College of Engineering 12/13/08 Hammerstrom 2 and Computer Science Moore’s Law: The number of transistors doubles every 18-24 months No discussion of computing is complete without addressing Moore’s law The semiconductor industry has been following it for almost 30 years It is not really a physical law, but one of faith The fruits of a hyper-competitive $300 billion global industry Then there is Moore’s lesser known 2nd law st The 1 law requires exponentially increasing investment And what I call Moore’s 3rd law st The 1 law results in exponentially increasing design errata Maseeh College of Engineering 12/13/08 Hammerstrom 3 and Computer Science Intel is now manufacturing in their new, innovative 45 nm process Effective gate lengths of 37 nm (HkMG) And they recently announced a 32 nm scaling of the 45 nm process Transistors of this size are no longer acting like ideal switches And there are other problems … 45 nm Transistor Maseeh College of Engineering 12/13/08 Hammerstrom 4 and Computer Science Projected Power Density Pat Gelsinger, ISSCC 2001 Maseeh College of Engineering 12/13/08 Hammerstrom 5 and Computer Science Performance overkill - the highest volume segments of the market are no longer performance/clock frequency driven Density overkill – How do we use all these transistors? The end of Moore’s law – scaling will continue, though at a decreasing rate, asymptotically approaching 22nm in 10-15 years Lithography will be the primary constraint going forward The current business model based on shrinks and compactions will change dramatically Maseeh College of Engineering 12/13/08 Hammerstrom 6 and Computer Science Parallelism Because of power and interconnect limitations, ever increasing processor performance will need to come more from parallel execution However, there are still few opportunities to leverage parallelism in volume market desktop applications And we have not yet solved the parallel computing problem Taking advantage of multiple cores will be much more difficult than taking advantage of faster clock speeds was Maseeh College of Engineering 12/13/08 Hammerstrom 7 and Computer Science The Complexity Crisis And the complexity of systems is growing exponentially According to a recent study by the NIST, “Software bugs" cost the U.S. economy an estimated $60B annually, 0.6% of the GNP In spite of the heroic efforts of computer scientists and engineers around the globe, we are slowly losing this battle As Bill Wulf said once, “software is getting slower faster than hardware is getting faster” Maseeh College of Engineering 12/13/08 Hammerstrom 8 and Computer Science The Design Productivity Gap Complexity is a problem for hardware too (recall Moore’s 3rd law) The “Gap” is the difference between the number of transistors that The typical design team using state of the art tools / methodologies can design and validate on a typical schedule And what’s available The Gap, therefore, results from the fact that the number of transistors is increasing faster than our ability to design them And how do we create a 100% guaranteed correct design of several billion transistors? Maseeh College of Engineering 12/13/08 Hammerstrom 9 and Computer Science “Post-CMOS” or “Nanoelectronics” The industry is now talking about “Post-CMOS” electronics, which usually means “nano” or “molecular” electronics Can we, by moving to molecular scale “electronics,” buy a little more shrinkage? Is it possible? Is it economical? What will do with it? Will it enable new applications? Or will it be more of the same? And most importantly, what should the research agenda be? Will we hit the complexity or capital investment walls before Moore’s law runs out? Maseeh College of Engineering 12/13/08 Hammerstrom 10 and Computer Science 12/13/08 11 12/13/08 12 Maseeh College of Engineering Hammerstrom and Computer Science Nanoelectronics You can get a good description of the basic candidates for molecular scale computing in the Emerging Research Devices chapter in the 2007 ITRS (the semiconductor roadmap) http://public.itrs.net/ We’re mostly interested in device who computations are based on charge Charge based technologies can more closely approximate the “charge accumulation” model common in most functional neural models Non charged based technologies must emulate charge accumulation digitally Maseeh College of Engineering 12/13/08 Hammerstrom 14 and Computer Science Of the various problems facing the semiconductor industry, which ones does nanotechnology solve? The end of Moore’s law Maybe the memory bandwidth problem? Anything else? It severely aggravates the design complexity problem – having trouble using billions of transistors? Well, we’re going to give you trillions! Oh, and did I mention that they will be flaky and slow? It is unlikely that our tools and methodologies will stretch far enough to handle these densities Maseeh College of Engineering 12/13/08 Hammerstrom and Computer15 Science And Nanotechnology also creates a number of new problems Significant levels of signal/clock delay (asynchronous logic is suddenly looking very appealing) Loving device variability? Wait until we get to the nano-scale! Manufacturing defects at a level not seen since the earliest days of the industry High dynamic failure rates during operation Fault detection and correction circuitry as a fundamental part of every design How do we handle this in the tools? How do we test such systems? But, the $64K question is, what exactly will we use nanoelectronics for? Maseeh College of Engineering 12/13/08 Hammerstrom and Computer16 Science Can we assume that computation, algorithms, and applications will continue more or less as they have? Should we? The effective use of nanotechnology will require solutions to more than just increased density, we need to consider total system solutions And you cannot create an architecture without some sense of the applications it will execute An architecture is not an end in itself, but a tool to solve a problem Any paradigm shift in applications and architecture, and I think we are headed into one, will have a profound impact on the whole design process and the tools required Maseeh College of Engineering 12/13/08 Hammerstrom and Computer17 Science Scaling It is very likely that sheer size is one of the major components of the “magic of human cognition” Consider the differences: hundreds of rules or thousands of nodes vs. billions of neurons Such “mega-algorithms” can be run on supercomputers But how do we deploy very large networks in small portable form factors that consume very little power and operate in real time? Massive parallelism in the models enables specialized hardware Maseeh College of Engineering 12/13/08 Hammerstrom 18 and Computer Science Radical new technologies create opportunities What if we could find an application space that, in addition to promising a solution to the Intelligent Computing problem, also addressed some of the other challenges facing the computer industry? One that exhibited massive parallelism low power density – where performance was based on parallelism not speed tolerance of static and dynamic faults, and even some design fault tolerance asynchrony (no clock) self-organization and adaptation, rather than being programmed Maseeh College of Engineering 12/13/08 Hammerstrom 19 and Computer Science We Need Nano, Nano Needs Us! The opportunity is real and it is coming! We need massively parallel algorithms to drive this effort and to justify the investment in the necessary architectures and implementation technology But, I believe that success in this area – this has the potential to be the “microprocessor” of the 21st century Biologically inspired algorithms are better positioned to leverage this opportunity than any other application domain Maseeh College of Engineering 12/13/08 Hammerstrom 20 and Computer Science The Most Promising “Post-CMOS” Candidate: Nanogrids On CMOS Simplistically: a nanogrid consists of A roughly horizontal group of nanowires A layer of some specialized chemical Another roughly vertical group of nanowires Connections of both groups of nanowires to CMOS metal lines Currently researchers are making wires out of silicon and other materials that are ~15 nm in diameter, eventually going to < 10 nm, with lengths up to 10 µm These are itsy bitsy wires and they have very high resistance, severely limiting their speed, but oh that density … Maseeh College of Engineering 12/13/08 Hammerstrom 21 and Computer Science CMOL – Developed by K. Likharev, SUNY Stony Brook Maseeh College of Engineering 12/13/08 Hammerstrom 22 and Computer Science The Molecular Switch – “Memrister” Where a horizontal wire crosses a vertical wire (which is self-aligned incidentally), molecules in the molecular layer form a switchable diode The switch is created from a few molecules G. Snider “Computing with hysteretic resistor
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