Training Of Physical Neural Networks Ali Momeni Babak Rahmani Benjamin Scellier Logan G Wright Peter L Mcmahon Clara C Wanjura Yuhang Li Anas Skalli Natalia G Berloff Tatsuhiro Onodera Ilker Oguz Francesco Morichetti Philipp Hougne Manuel Gallo Abu by Ali Momeni & Babak Rahmani & Benjamin Scellier & Logan G. Wright & Peter L. Mcmahon & Clara C. Wanjura & Yuhang Li & Anas Skalli & Natalia G. Berloff & Tatsuhiro Onodera & Ilker Oguz & Francesco Morichetti & Philipp Hougne & Manuel Gallo & Abu… instant download after payment.

Physical neural networks (PNNs) are a class of neural-like networks that make use of analogue physical systems to perform computations. Although at present confned to small-scale laboratory demonstrations, PNNs could one day transform how artifcial intelligence (AI) calculations are performed. Could we train AI models many orders of magnitude larger than present ones? Could we perform model inference locally and privately on edge devices? Research over the past few years has shown that the answer to these questions is probably “yes, with enough research”. Because PNNs can make use of analogue physical computations more directly, fexibly and opportunistically than traditional computing hardware, they could change what is possible and practical for AI systems. To do this, however, will require notable progress, rethinking both how AI models work and how they are trained—primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs, backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-ofs and, so far, no method has been shown to scale to large models with the same performance as the backpropagation algorithm widely used in deep learning today. However, this challenge has been rapidly changing and a diverse ecosystem of training techniques provides clues for how PNNs may one day be used to create both more efcient and larger-scale realizations of present-scale AI models.