Improve Robustness of Deep Neural Networks by Coding

Abstract

Deep neural networks (DNNs) typically have many weights. When errors appear in their weights, which are usually stored in non-volatile memories, their performance can degrade significantly. We review two recently presented approaches that improve the robustness of DNNs in complementary ways. In the first approach, we use error-correcting codes as external redundancy to protect the weights from errors. A deep reinforcement learning algorithm is used to optimize the redundancy-performance tradeoff. In the second approach, internal redundancy is added to neurons via coding. It enables neurons to perform robust inference in noisy environments.

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