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An Analog Network of Resistors Promises Machine Learning Without a Processor
Prototyped on a series of breadboards, this analog machine learning network could one day deliver more energy-efficient AI.
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. Better still, it shows the potential to outperform the traditional approach of throwing the problems at digital processors: "We find our non-linear learning metamaterial reduces modes of training error in order (mean, slope, curvature)," the team claims, "similar to spectral bias in artificial neural networks." There is, of course, a catch: in its current form, existing as a prototype spread across a series of solderless breadboards, the metamaterial system draws around ten times the power of a state-of-the-art digital machine learning accelerator — but as it scales, Dillavou says, the technology should deliver on a promise of increased efficiency and the ability to remove external memory components from the bill of materials.
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