Cornell & NTT’s Physical Neural Networks: a “Radical Alternative for Implementing Deep Neural Networks” That Enables Arbitrary Physical Systems Training

A team from Cornell University and NTT Research proposes Physical Neural Networks (PNNs), a universal framework that leverages a backpropagation algorithm to train arbitrary, real physical systems to execute deep neural networks.

Deep neural networks (DNNs) already provide the best solutions for many complex problems in image recognition, speech recognition, and natural language processing. Now, DNNs are entering the physical arena. DNNs and physical processes share numerous structural similarities, such as hierarchy, approximate symmetries, redundancy and nonlinearity, suggesting the potential for DNNs to operate effectively on data from the physical world. In the paper Deep Physical Neural Networks Enabled by a Backpropagation Algorithm for Arbitrary Physical Systems, a research team from Cornell University and NTT Research proposes that the controlled evolutions of physical systems are well-suited to the realization of deep learning models, and introduces Physical Neural Networks (PNN), a novel framework that leverages a backpropagation algorithm to train arbitrary, real physical systems to execute deep neural networks.

The principle behind backpropagation algorithms is the modelling of mathematical operations by modifying input signal weights to produce an expected output signal. Determining the optimal parameter updates makes it possible to improve model performance by computing the gradient descend. The proposed PNN framework is enabled by a Physics-Aware Training (PAT) approach based on a novel hybrid physical-digital algorithm that can execute the backpropagation algorithm efficiently and accurately on any sequence of physical input-output transformations. Essentially this means a problem is solved by applying backpropagation algorithms to train sequences of real physical operations to perform desired physical functions.

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