Deep neural networks have become a pervasive tool in science and engineering.
However, modern deep neural networks’ growing energy requirements now
increasingly limit their scaling and broader use. We propose a radical
alternative for implementing deep neural network models: Physical Neural
Networks. We introduce a hybrid physical-digital algorithm called Physics-Aware
Training to efficiently train sequences of controllable physical systems to act
as deep neural networks. This method automatically trains the functionality of
any sequence of real physical systems, directly, using backpropagation, the
same technique used for modern deep neural networks. To illustrate their
generality, we demonstrate physical neural networks with three diverse physical
systems-optical, mechanical, and electrical. Physical neural networks may
facilitate unconventional machine learning hardware that is orders of magnitude
faster and more energy efficient than conventional electronic processors.