The Power of Disorder: Advancing Neuromorphic Computing through Analog Improvements

Saturday - April 20 2024, 18:58 UTC - 7 months ago

Advancements in neuromorphic computing aim to mimic the power efficiency and associative processing of the human brain. Research from UC San Diego and UC Riverside is exploring the use of disordered superconducting loops to store and transmit information. This has the potential to significantly reduce power consumption and increase processing speed.

Computers work in digits — 0s and 1s to be exact. Their calculations are digital; their processes are digital; even their memories are digital. All of which requires extraordinary power resources. As we look to the next evolution of computing and developing neuromorphic or “brain-like” computing, those power requirements are unfeasible.

To advance neuromorphic computing, some researchers are looking at analog improvements. In other words, not just advancing software, but advancing hardware too. Research from the University of California San Diego and UC Riverside shows a promising new way to store and transmit information using disordered superconducting loops.

The team’s research, which appears in the Proceedings of the National Academy of Sciences, offers the ability of superconducting loops to demonstrate associative memory, which, in humans, allows the brain to remember the relationship between two unrelated items.

“I hope what we’re designing, simulating and building will be able to do that kind of associative processing really fast,” stated UC San Diego Professor of Physics Robert C. Dynes, who is one of the paper’s co-authors.

Picture it: you’re at a party and run into someone you haven’t seen in a while. You know their name but can’t quite recall it. Your brain starts to root around for the information: where did I meet this person? How were we introduced? If you’re lucky, your brain finds the pathway to retrieve what was missing. Sometimes, of course, you’re unlucky.

Dynes believes that short-term memory moves into long-term memory with repetition. In the case of a name, the more you see the person and use the name, the more deeply it is written into memory. This is why we still remember a song from when we were ten years old but can’t remember what we had for lunch yesterday.

“Our brains have this remarkable gift of associative memory, which we don’t really understand,” stated Dynes, who is also president emeritus of the University of California and former UC San Diego chancellor. “It can work through the probability of answers because it’s so highly interconnected. This computer brain we built and modeled is also highly interactive. If you input a signal, the whole computer brain knows you did it.” .

How do disordered superconducting loops work? You need a superconducting material — in this case, the team used yttrium barium copper oxide (YBCO). Known as a high-temperature superconductor, YBCO becomes superconducting around 90 Kelvin (-297 F), which in the world of physics, is not that cold. This made it relatively easy to modify. The YBCO thin films (about 10 microns wide) were manipulated with a combination of magnetic fields and currents to create a single flux quantum on the loop. When the current was removed, the flux quantum stayed in the loop. Think of this as a piece of information or memory.

This is one loop, but associative memory and processing require at least two pieces of information. For this, Dynes used disordered loops, meaning the loops are different sises. The team found that as current pulses were sent to successful disordered loops, the current was steady when it got to the memory loop and there was a sharp pulse when not successful. These findings could contribute to building a neuromorphic computer.