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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 aXis100 (690904) on Tuesday April 29, 2014 @08:03PM (#46873909)

    Good old clueless tech journalists, followed by slashdot editors just copy pasting.

    The chips aren't 9000 times faster than a typical PC for general tasks. Specifically, they can simulate neurons 9000 times faster than a PC can simulate neurons. Pretty typical of any ASIC with a limited set of a highly specialised functions.

  • by timeOday (582209) on Tuesday April 29, 2014 @08:45PM (#46874199)
    9000 times faster than a PC, if that PC happens to be running the specific artificial neural network simulation implemented in hardware by this chip.

    Not that I'm knocking it. A GPU implements specific algorithms to great effect. But a GPU's algorithms are ones that are interesting for a specific application (drawing texture-mapped polygons), whereas an artificial neural network still needs another layer of programming to do something useful. In other words, a Word Processor implemented on this chip would not be 9000x faster than a Word Processor implemented on a CPU. A face recognition algorithm, on the other hand, might see a decent fraction of that 9000x, although it remains to be seen whether this chip would be a better fit for any particular application than a GPU (for example).

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