What Is Neuromorphic Computing? Everything You Need To Know About How It Is Changing the Future of Computing
Neuromorphic computing could completely transform everything about the technology industry from programming languages to hardware.
What Is Neuromorphic Computing?
As the name suggests, neuromorphic computing uses a model that's inspired by the workings of the brain.
The brain makes a really appealing model for computing: Unlike most supercomputers, which fill rooms, the brain is compact, fitting neatly in something the size of, well... your head.
Brains also need far less energy than most supercomputers: Your brain uses about 20 watts, whereas the Fugaku supercomputer needs 28 megawatts—or, to put it another way, a brain needs about 0.00007% of Fugaku's power supply. While supercomputers need elaborate cooling systems, the brain sits in a bony housing that keeps it neatly at 37°C.
True, supercomputers make specific calculations at great speed, but the brain wins on adaptability. It can write poetry, pick a familiar face out of a crowd in a flash, drive a car, learn a new language, take good decisions and bad, and so much more. And, with traditional models of computing struggling, harnessing techniques used by our brains could be the key to vastly more powerful computers in the future.
Why Do We Need Neuromorphic Systems?
Most hardware today is based on the von Neumann architecture, which separates out memory and computing. Because von Neumann chips have to shuttle information back and forth between the memory and central processing unit, they waste time (computations are held back by the speed of the bus between the compute and memory) and energy—a problem known as the von Neumann bottleneck.
By cramming more transistors onto these von Neumann processors, chipmakers have for a long time been able to keep adding to the amount of computing power on a chip, following Moore's Law. But problems with shrinking transistors any further, their energy requirements, and the heat they throw out mean that, without a change in chip fundamentals, that won't go on for much longer.
As time goes on, von Neumann architectures will make it harder and harder to deliver the increases in compute power that we need.
To keep up, a new type of non-von Neumann architecture will be needed: a neuromorphic architecture. Quantum computing and neuromorphic systems have both been claimed as the solution, and it's neuromorphic computing, brain-inspired computing, that's likely to be commercialized sooner.
As well as potentially overcoming the von Neumann bottleneck, a neuromorphic computer could channel the brain's workings to address other problems. While von Neumann systems are largely serial, brains use massively parallel computing. Brains are also more fault-tolerant than computers—both advantages researchers are hoping to model within neuromorphic systems.