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Supercomputing

Stanford Bioengineers Develop 'Neurocore' Chips 9,000 Times Faster Than a PC 209

Posted by Soulskill
from the i'll-order-a-dozen dept.
kelk1 sends this article from the Stanford News Service: "Stanford bioengineers have developed faster, more energy-efficient microchips based on the human brain – 9,000 times faster and using significantly less power than a typical PC (abstract). Kwabena Boahen and his team have developed Neurogrid, a circuit board consisting of 16 custom-designed 'Neurocore' chips. Together these 16 chips can simulate 1 million neurons and billions of synaptic connections. The team designed these chips with power efficiency in mind. Their strategy was to enable certain synapses to share hardware circuits. ... But much work lies ahead. Each of the current million-neuron Neurogrid circuit boards cost about $40,000. (...) Neurogrid is based on 16 Neurocores, each of which supports 65,536 neurons. Those chips were made using 15-year-old fabrication technologies. By switching to modern manufacturing processes and fabricating the chips in large volumes, he could cut a Neurocore's cost 100-fold – suggesting a million-neuron board for $400 a copy."
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Stanford Bioengineers Develop 'Neurocore' Chips 9,000 Times Faster Than a PC

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  • by Anonymous Coward on Tuesday April 29, 2014 @07:32PM (#46873685)

    I've just seen three comments deleted within 5 minutes complaining about the lack of journalism.... Copy and pasting a press release headline without any real reporting is what tabloids are for.

  • Re:crysis? (Score:3, Interesting)

    by aXis100 (690904) on Tuesday April 29, 2014 @08:06PM (#46873921)

    The article is misleading - they are not 9000 times faster than a PC for general tasks. The chips can simulate neurons 9000 times faster than a PC can simulate neurons, but there's no mention of how fast those simulated neurons can solve a problem for you.

  • Re:Here it comes. (Score:3, Interesting)

    by Anonymous Coward on Tuesday April 29, 2014 @08:25PM (#46874065)

    Neurons have incredibly complex behaviors, they are not simply threshold triggers as the simple CS model implies. Neural networks in CS have little to do with the actual wiring and primarily chemical systems that are neurons. A little bit of cognitive neuroscience taught in universites would cure most CS majors of this idea that they can get AI simply with a "neural" net made of simple triggering model neurons.

  • by Immerman (2627577) on Tuesday April 29, 2014 @08:39PM (#46874169)

    I doubt it. Well, at least not as soon as you might imagine. "Together these 16 chips can simulate 1 million neurons and billions of synaptic connections"

    Total number of neurons in cerebral cortex =
      --10 billion (from G.M. Shepherd, The Synaptic Organization of the Brain, 1998, p. 6).
      --20 billion (Biophysics of Computation. Information Processing in Single Neurons, New York: Oxford Univ. Press, 1999, page 87).
    Total number of synapses in cerebral cortex
      -- 60 trillion (from G.M. Shepherd, The Synaptic Organization of the Brain, 1998, p. 6).
      --150 trillion (Pakkenberg et al., 1997; 2003)
      --240 trillion (Biophysics of Computation. Information Processing in Single Neurons, New York: Oxford Univ. Press, 1999, page 87).

    So, lets call it 15 billion neurons and 150 trillion synapses, or tens of thousands of synapses per neuron, ten times as many as this chip provides. That's going to be a problem. To say nothing of the fact that I would be very surprised if it allows for billions of inter-chip synapses which would probably be necessary to model the non-local interconnections common in the brain within the 240,000 chip brain simulator. And that's just for the cerebral cortex. You've got the rest of the brain to simulate as well.

    Then there's the glial cells, which outnumber neurons by 10-50:1, and which recent research suggests may be considerably more involved in neural activity than presumed by the traditional "life support and other infrastructure" understanding.

    Could be great for modeling larger portions of a mouse brain though. Maybe even to start modeling the simpler parts of a human brain. And we do have to start somewhere. I suspect we're at least a few decades away from being able to begin to simulate an entire human brain, and probably many more decades away from getting the simulation accurate enough that it might begin to actually function properly. After all the number one benefit of these simulations is to fail spectacularly in interesting way in order to help neuroscientists figure out what questions they should be asking.

    Meanwhile we need to ask ourselves - if we're creating this simulation based on the human brain, then what are the odds that some form of consciousness dwells within it? And what sort of torture are we subjecting it to as it's simulation collapses? And does the knowledge we gain justify that price?

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