Physical neural networks (PNNs) are analog computing systems based on electronic, optical, or even biological hardware rather than computer chips. PNNs can potentially perform as well as conventional AI systems. But they consume less energy while being more resistant to the effects of noisy environments. Now Satoshi Sunada of Kanazawa University in Japan and his colleagues have developed a training protocol that addresses some of the challenges for PNN training [1]. In tests, the researchers found that an optoelectronic circuit trained using their protocol performed as well as conventional neural networks. They say their protocol should allow a wider variety of physical systems to serve as PNN computing platforms.
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