Human ingenuity has given birth to incredibly powerful computers that can plow through more calculations in a second than most people could in their entire lives, but computers still aren’t terribly adaptable. The human brain is a very different kind of computer — a massively parallel processor that has been shaped by millions of years of evolution to recognize patterns and adjust to changing situations. This is the kind of capability computer science researchers are now trying to unlock, and scientists at Sandia National Laboratories are stepping up their game to design neuro-inspired, orneuromorphic, computer systems.
Sandia isn’t just attracted to the idea of computers designed like brains because of the capabilities, but the human brain is also incredibly efficient. A computer has trouble telling the difference between a picture of a dog and a cat, but it eats up hundreds of watts of power simply trying. A brain, by contrast, operates continuously for decades and only consumes roughly the same power as a 20-watt light bulb.
Of all the powers that we have imagined for the cyborg, which do we most covet? Their ability to see and sense detail in the environment? The ability manipulate things with the dexterity and power of a machine? Or perhaps it would be to command vast amounts of information which can be processed at tremendous speed?
If you chose none of those, you chose as any cyborg likely would have. The cyborg’s greatest power, that from which it derives the most satisfaction (to use that term loosely), must be the ability to see itself. As humans, we are a mystery unto ourselves. If we were suddenly presented with one of our own organs from beneath our skin, before the panic set in, we would be taken by the awe and mystery that a mother must feel after the delivery of her child. To know the mass inside our skull will be to know ourselves — and to control what we might become.
Complementary metal-oxide-semiconductor (CMOS) transistors are commonly used in very-large-scale-integration (VLSI) digital circuits as a basic binary switch that turns on or off as the transistor gate voltage crosses some threshold. Carver Mead first noted that CMOS transistor circuits operating below this threshold in current mode have strikingly similar sigmoidal current–voltage relationships as do neuronal ion channels and consume little power; hence they are ideal analogs of neuronal function.
At the International Joint Conference on Neural Networks held this week in Dallas, researchers from IBM have taken the wraps off a new software front-end for its neuromorphic processor chips. The ultimate goal of these most recent efforts is to recast Watson-style cognitive computing, and its recent successes, into a decidedly more efficient architecture inspired by the brain. As we shall see, the researchers have their work cut out for them — building something that on the surface looks like the brain is a lot different from building something that acts like the brain.
Head researcher of IBM’s Cognitive Computing group, Dharmendra Modha, announced last November that his group had simulated over 500 billion neurons using the Blue Gene/Sequoia supercomputer at the Lawrence Livermore National Laboratory (LLNL). His claims, however, continue to draw criticism from others who say that the representation of these neurons is too simplistic. In other words, the model neurons generate spikes like real neurons, but the underlying activity that creates those spikes is not modeled in sufficient detail, nor are the details of connections between them.
Scientists at IBM Research have created by far the most advanced neuromorphic (brain-like) computer chip to date. The chip, called TrueNorth, consists of 1 million programmable neurons and 256 million programmable synapses across 4096 individual neurosynaptic cores. Built on Samsung’s 28nm process and with a monstrous transistor count of 5.4 billion, this is one of the largest and most advanced computer chips ever made. Perhaps most importantly, though, TrueNorth is incredibly efficient: The chip consumes just 72 milliwatts at max load, which equates to around 400 billion synaptic operations per second per watt — or about 176,000 times more efficient than a modern CPU running the same brain-like workload, or 769 times more efficient than other state-of-the-art neuromorphic approaches. Yes, IBM is now a big step closer to building a brain on a chip.
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