Researchers from the University of Pennsylvania have come up with an interesting approach to machine learning that could help to address the field

An Analog Network of Resistors Promises "Machine Learning Without a Processor," Researchers Say

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2024-06-30 10:00:34

Researchers from the University of Pennsylvania have come up with an interesting approach to machine learning that could help to address the field's ever-growing power demands: taking the processor out of the picture and working directly on an analog network of resistors.

"Standard deep learning algorithms require differentiating large non-linear networks, a process that is slow and power-hungry," the researchers explain. "Electronic learning metamaterials offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating non-linear elements have not been explored."

Until now, that is. In the team's research, a non-linear learning metamaterial is introduced — an analog electronic network of resistive elements based on transistors. It's not a traditional digital processor, and can't do the tasks a traditional processor can do — but it is tailored specifically to machine learning workloads, and proved able to perform computations that can't be handled in a linear system without the involvement of a processor beyond an Arduino Due to make measurements and connect to MATLAB.