Introduction to Neuromorphic Computing
As artificial intelligence platforms like OpenAI’s ChatGPT and Microsoft’s Copilot go mainstream, power bills from their usage are exploding. In response, researchers are racing to build hardware that would guzzle less energy. One such effort is underway at the University of Texas at Dallas. Working with Texas Instruments and Arizona-based Everspin Technologies, scientists there have built a small neuromorphic computer system — a brain-inspired design — that uses tiny magnetic “sandwiches” inside its chips to mimic the behavior of neurons. In lab tests, an AI program running on this hardware recognized tiny black-and-white images while using less circuitry and energy compared to today’s AI systems.
The findings of this prototype, published in the journal Communications Engineering, are “a tour de force” not just in how AI models run but in how they learn — the energy-hungry process that teaches these algorithms to make good predictions, said Mark Stiles, a computer scientist at the National Institute of Standards and Technology in Washington, D.C., who was not involved in the study.
History of Neuromorphic Computing
Imitating human brains in computing isn’t a new idea. In the late 1950s, the U.S. Office of Naval Research unveiled the “perceptron,” a 5-ton, room-sized machine that, after about 50 trials, taught itself to identify punch cards marked on either the left or right. The computer relied on a single-layer neural network, an algorithm that learns through trial and error to tell which of two categories an input belongs to.
Decades later, the perceptron’s design would inspire deep learning, a kind of AI that finds patterns in data by running it through many layers of artificial neurons, also known as nodes. (Each neural layer receives data, processes it and sends it on to the next layer.) Deep learning revolutionized AI and is now commonplace, curating social media feeds, powering image recognition and more.
Energy Consumption of AI Systems
But that kind of intelligence, along with other generative AI models, comes with a hefty energy bill. Training OpenAI’s GPT-3, for example, consumes about as much electricity as powering an average U.S. household for 120 years. One estimate says ChatGPT’s daily queries from millions of users use enough energy to charge thousands of electric vehicles a day and power roughly 29,000 U.S. homes for a year.
Some efforts to slash energy costs focus on using renewable sources, or on slimming down the AI models themselves. But engineers and computer scientists are also looking to neuromorphic computing — first conceived of in the late 1980s — as another way to offset AI’s energy boom.
How Neuromorphic Computing Works
At UT Dallas, Joseph Friedman, an associate professor of electrical and computer engineering, is making neuromorphic computer chips that process information like human neurons and store it locally, like synapses. Synapses pass a signal to the next neuron and convey how strong that signal is. In the human brain, studies suggest that at least some types of memory are stored in synapses.
One of the biggest hurdles for the researchers is copying how synapses store the strength of a connection, Friedman said. Signals between neurons in the brain aren’t simply binary — on or off, like in a conventional computer. Synapses can be stronger or weaker, adjusted like a volume dial on a boombox through different chemicals.
Joseph Friedman is an associate professor of electrical and computer engineering at the University of Texas at Dallas.
UT Dallas
Storing that kind of analog data in computer hardware is messy and error-prone, Friedman said. His team instead uses magnetic tunnel junctions, or tiny magnetic sandwiches made of two magnetic layers separated by a thin barrier. Electrons can travel through that barrier easily when the magnets line up and less so when they point in opposite directions, making each junction act like an on-and-off switch. The overall signal between artificial neurons can be strengthened or weakened by flipping on more or fewer of these switches.
Prototype Development
Friedman and his colleagues wired together eight of these magnetic sandwiches into a prototype computer system running an AI image-recognition model. The black-and-white images they asked the AI to distinguish were simple and small — just four pixels large, or roughly the size of a speck on a TV screen.
That task might not seem like much, but when the team pitted the setup against a conventional AI system, it learned the patterns and made predictions with less total energy — in part because it could store its memory within the neuromorphic chips.
The Need to Scale
While the prototype is small, Friedman said that once the neuromorphic system is built at a large scale, “we’re shooting for on the order of 100 to 1,000 times more energy efficiency” compared with electronic circuits called graphics processing units, such as those produced by California-based tech giant Nvidia.
