How neuromorphic ‘brain chips’ will begin the next era in computing

How neuromorphic ‘brain chips’ will begin the next era in computing

How neuromorphic ‘brain chips’ will begin the next era in computing
IBM recently released new details about the efficiency of its TrueNorth processors, which sport a fundamentally novel design that cribs from the structure of the brain. Rather than line up billions of digital transistors all in a line, TrueNorth chips have a million computer ‘neurons’ that work in parallel across 256 million inter-neuron connections (‘synapses’). According to these reports, the approach is paying incredible dividends in terms of performance and, more importantly, power efficiency. Make no mistake: neuromorphic computing is going to change the world, and it’s going to do it more quickly than you might imagine.

The development of neuromorphic computers is thematically pretty similar to the development of digital computers: First figure out the utility of an operation (say, computing firing trajectories during wartime), then develop a crude way of doing it with the tools you already have available (say, rooms full of people doing manual arithmetic), then invent a machine to automate this process in a much more efficient way. Part of the reason a digital computer is more efficient than a human being is its transistors can fire with incredible speed — but so can our neurons. The bigger issue is a digital computer is designed from the ground up to do those sorts of mathematical operations; from a certain perspective, it’s a bit crazy we ever tried to do efficient mathematical work on a computer like the human brain.

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Similarly, we will eventually look back at the attempt to do learning operations with digital chips, including GPUs, as inherently unwise or even silly. The much more reasonable approach is to design a thinking machine suited to such operations from the most basic hardware level, as naturally predisposed to machine learning as a Celeron chip is to multiplication. This could not only greatly increase the speed of the processor for these tasks, but dramatically reduce the energy consumed to complete each one. That’s what IBM has in the works, and it’s much further along than many expect.

When tasked with classifying images (a well understood machine learning task), a TrueNorth chip can churn through between 1,200 and 2,600 frames every second, and do it while using between 25 and 275 mW. This leads to an effective efficiency of more than 6,000 frames per second per Watt. There’s no listed standard frames/second/Watt figure for conventional GPUs using the same sorting algorithm and dataset, but considering modern graphics cards might draw 200 or even 250 watts all on their own, it’s hard not to imagine a host of low-power, high-performance applications.

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Most obviously, there is the incredible expense of modern machine learning. Companies like Apple, Facebook, and Google can only deliver their advanced services by running expensive arrays of super-computers designed to execute the machine learning algorithms as efficiently as possible, and that specialization comes at a crushing cost. Even leaving that aside, electricity alone becomes a major expense when you’re running that many computers at or near their capacity, 24 hours a day. Just ask Bitcoin miners.

So, early, expensive neuromorphic hardware will likely be a major boon to service providers, and we can only hope this will be passed along to consumers in the form of improved performance and wide-ranging savings. But the speed and efficiency offered by neuromorphic chips won’t stop there — reducing power draw by several orders of magnitude will allow such tasks to come out of the cloud entirely.

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