Researchers are exploring analog approaches to neuromorphic computing to address the high power demands of digital systems. A promising method from the University of California involves using disordered superconducting loops to store and transmit information, potentially enabling energy-efficient associative memory similar to human brain function.Computers work in digits — 0s and 1s to be exact. Their calculations are digital; their processes are digital; even their memories are digital.
“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.
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 sizes and follow different patterns — essentially random. The number of memory locations available increases exponentially with more loops: one loop has three locations, but three loops have 27. For this research, the team built four loops with 81 locations. Next, Dynes would like to expand the number of loops and the number memory locations.
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