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<p>Physical neural networks (PNNs) are a class of neural-like networks that make use of analogue physical systems to perform computations. Although at present confined to small-scale laboratory demonstrations, PNNs could one day transform how artificial 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, flexibly 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-offs 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 efficient and larger-scale realizations of present-scale AI models.</p>

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Training of physical neural networks

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • 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 del Hougne,
  • Manuel Le Gallo,
  • Abu Sebastian,
  • Azalia Mirhoseini,
  • Cheng Zhang,
  • Danijela Marković,
  • Daniel Brunner,
  • Christophe Moser,
  • Sylvain Gigan,
  • Florian Marquardt,
  • Aydogan Ozcan,
  • Julie Grollier,
  • Andrea J. Liu,
  • Demetri Psaltis,
  • Andrea Alù,
  • Romain Fleury

摘要

Physical neural networks (PNNs) are a class of neural-like networks that make use of analogue physical systems to perform computations. Although at present confined to small-scale laboratory demonstrations, PNNs could one day transform how artificial 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, flexibly 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-offs 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 efficient and larger-scale realizations of present-scale AI models.