NERVES OF STEEL

How Intel is taking inspiration from our neurons for next-gen chips

A single board from within the Pohoiki Springs box. Intel hasn’t given details about how the multiple chips, or their boards, are connected together.

Intel loves a good codename. Who remembers Dragontail Peak? Or Lizard Head Pass? Or even 2008’s White Salmon? Great days. All of those refer to motherboards, but Pohoiki Beach is different—it’s a new way of building computers that’s based on the human brain. Neuromorphic computing—literally ‘nerve shaped’—uses insights from neuroscience to create chip architectures.

By simulating the way human brains work in silicon, calculations can be carried out faster while using less energy. The training of neural networks can be carried out more efficiently too, with only one viewing of an object necessary for the net to recognize it forever.

Mike Davies, director of Intel’s Neuromorphic Computing Lab, sees it more clearly, “Neuromorphic computing entails nothing less than a bottom-up rethinking of computer architecture,” he says. “The goal is to create chips that function less like a classical computer and more like a human brain. Neuromorphic chips model how the brain’s neurones communicate and learn, using spikes and plastic synapses that can be modulated based on the timing of events. These chips are designed to self-organize and make decisions in response to learned patterns and associations.”

Which all sounds a bit Cyberdyne, but we’re sure this will be fine. The goal is that one day neuromorphic chips may be able to learn as fast and efficiently as the brain, which still far outperforms today’s most powerful computers. According to Intel, neuromorphic computing could lead to advancements in robotics, smart city infrastructure, and other applications that require continuous learning and adaptation to evolving data.

“The inspiration for neuromorphic computing goes back to the earliest days of computing itself,” says Davies. “If you look at the early papers by John Von Neumann or Alan Turing, they actually talk about neurones and synapses, because back in the ’40s they hadn’t invented the terminology of conventional computing. The brain was the one example they had.”

And while classical computing has been solving problems for 80 years, mother nature has been at it for billions, and has got quite good at making brains. “If you look at the human brain,” says Davies, “it operates at 20W. Everything we do— simultaneously processing data streams, coming up with new ideas and insights—all that is being done at just 20W of power”. For context, a Raspberry Pi 4B pulls 7.6W under load, but an i7-7700K can draw 77W while doing a bit of light gaming. Now, the i7’s Kaby Lake cores probably do a bit more work per second than the Cortex-A72s powering the Pi, but it goes to show how power efficient the human brain is. Also it runs on glucose and doesn’t need a fan and heatsink arrangement to be bolted to the top.

The 770 processors in Pohoiki Springs—which is the second generation of the technology after Pohoiki Beach—are