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.

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

The paper Deep Physical Neural Networks Enabled by a Backpropagation Algorithm for Arbitrary Physical Systems is on arXiv.



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