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Andre dehon phd thesis | oberunistolrosystwitlampneteb
Center for. WWW CRN ONLY New! Nanotech Scenario Series. Join the conversation at CRNtalk! Sander Olson Interviews. André DeHon. CONDUCTED OCTOBER André DeHon is a Professor of Computer Science at the California Institute of Technology. He has done pioneering research on many aspects andre dehon phd thesis computation, including sublithographic computing, interconnects, and Programmable Logic Arrays.
Question 1: Tell us about yourself. What is your background, and what projects are you currently working on? I won't do that one justice here. My web page and publication record should give many hints about that. I double majored in EE and CS at the undergraduate level because I wanted to design computers and, at the time, andre dehon phd thesis, VLSI and electrical circuits where the primary media for doing that.
I increasingly believe in the fundamental interaction between the substrate and its cost structure and what architectures microarchitectures are efficient for it. This is what led me into reconfigurable work, andre dehon phd thesis. In hindsight, I can say that the silicon cost structure outgrew processors in the mids.
FPGAs and FPGA-computing were the hint that there was something bigger to explore. In addition to the substrate cost, the mapping tools play a big role in the architecture you want to build. So, starting as an architect, I've expanded to understand substrates, architecture, andre dehon phd thesis, tools, run-time systems, and programming modelsall the things that play into building a computer system.
I continue to aspire to be the "Tall Thin Architect" which Carver Mead described. This sublithographic area is interesting to me as I see it changing the substrate costs even further.
How does that change the efficient architectures? If Howard Roark had been a Computer Architect, he might have asked:. Are andre dehon phd thesis making copies in submicron CMOS of copies in early NMOS of copies in discrete TTL of vacuum tube computers?
When we design with nanowires or nanotubes, should we make copies in andre dehon phd thesis of copies in submicron CMOS of copies in early NMOS of copies in discrete TTL of vacuum tube computers?
Question 2: How long do you believe that classical semiconductor scaling can continue? Do you think that we will ever see 20 nanometer transistors in mass production? I guess I should be clear, I'm an academic not an economist. I wouldn't bet against the semiconductor industry. However, I think there may be some good alternatives to pushing the current lithographic model.
I hope we can demonstrate some better options before the industry gets there. Perhaps, the stuff we're doing will be how they produce those 20nm and smaller transistors in mass production? Question 3: Many proposals for molecular electronics have been proposed, andre dehon phd thesis.
Which molecular electronics paradigm do you believe has the greatest chance of success? Within the stuff we're looking at there are versions that are all silicon or GaN These things are potentially quite compatible with the existing, silicon infrastructure. In the short run, at least, this looks like the most promising thing. I see paths to introduce these components incrementally into the traditional silicon manufacturing flow.
Question 4: There are many companies examining various molecular memory schemes, but molecular logic is a much dicier proposition. Are there molecular electronics technologies that exhibit good amplification gain? The nanowires that Charles Lieber builds, and we've been designing withappear to exhibit sufficient gain for logic operation, but one could quibble as to whether or not this is "molecular".
It's certainly "molecular-scale," and the SiNW gain elements here are compatible with using molecules for the switches, if that turns out to be the best way to build the switches. We just don't depend on the programmable switches for gain. One architecture is laid out pretty clearly in our recent FPGA paper.
Question 5: Tell us about configurable computing. Could Field-Programmable Gate Array FPGA chips ever be reprogrammed "on the fly", andre dehon phd thesis, or be used for general computing tasks? Could FPGAs ever render CPUs obsolete? That first part, andre dehon phd thesis, at least was my PhD thesis 8 years ago. For a more concise version of why you want to use FPGA-like things, see: André DeHon.
The Density Advantage of Configurable Computing. IEEE Computer, 33 4April FPGAs alone won't render CPUs obsolete. They complement andre dehon phd thesis. The real question is what percentage of your die area should be FPGA-like logic versus CPU-like logic.
I andre dehon phd thesis there's an argument that, over time, the percentage of area on the die going into CPU-like logic diminishes. If there's something that renders CPUs obsolete, it may be sublithographic fabricationwhich appears to favor building things andre dehon phd thesis look more like FPGAs that CPUs.
The work around my PhD actually contained the most aggressive approaches for "on the fly" reconfiguration, andre dehon phd thesis. My more recent work looks at other options and begins to deal with programming models for this and supporting run-time routines to make it profitable:.
Question 6: One of the most severe problems plaguing modern computing systems is heat dissipation. Some modern CPUs already dissipate up to watts, and this problem will only get worse. Can FPGAs ameliorate this problem?
They may be able to help, but bigger measures are necessary to tackle both processor andre dehon phd thesis FPGA energy consumption. Some numbers I ran a while back, suggested raw bit-op per bit-op the CPU and the FPGA were pretty close in energy requirements in conventional siliconmaking worse-case assumptions.
There's an argument I can make that the FPGA may be able to better exploit correlation in the data to reduce the activity factor and this can produce a big win. Jan Rabaey had some data comparing processors and FPGAs that showed them coming about almost an order of magnitude lower net energy per operation compared to low-energy processor designs like the StrongARM. I think that comes from this correlation effect. However, I think that's not enough of a win for the problem to go away.
I think both processors and FPGAs will need to start looking at techniques like adiabatic switching. Question 7: What is your opinion of the concept of 3-dimensional computing? Will it ever be feasible? How will computers deal with heat buildup issues? I have ideas on how to make it feasible. There may be a synergy between nanowires and 3D computing structures. This is where I think adiabatic switching is most motivated, andre dehon phd thesis.
If 3D gives us more area such that heat-density is the key limiter to useful computational density, let's trade some area for reduced energy. I think we can find a sweet-spot which provides greater computational density than simply staying with 2D. But, there's definitely quite a bit of research needed to work this out a rich area to explore. Question 8: What is your opinion of artificial intelligence?
Do you believe that we will ever see truly sentient machine systems? Understanding intelligence is a great goal. Automation is good -- it's what computing is all about. We will see much smarter machines than we have today, andre dehon phd thesis.
I'm not sure if we understand "sentience" enough, yet, to be able to judge if a machine is sentient but trying to understand that is also a laudable goal. In an increasing number of focused domains, we'll certainly see things that start looking pretty sentient. Question 9: The researcher Hugo Degaris wants to use FPGAs to make "evolvable hardware".
In essence, this hardware would alter its circuitry to perform tasks better. What is your opinion of evolvable hardware? As far as you can say that "FPGA circuits can be altered", computer systems that "alter its circuitry to perform tasks better" will happen.
I believe in that. I think we're seeing the first glimpses of how to do that and we have a long way to go.
By the way, are the circuits being altered? Or are we just loading in different instructions? Universal Turing equivalence tells us, in the end, it doesn't matter.
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