Stochastic Computing Architectures for Lightweight LSTM Neural Networks

For emerging edge and near-sensor systems to perform hard classification tasks locally, they must avoid costly communication with the cloud. This requires the use of compact classifiers such as recurrent neural networks of the long short term memory (LSTM) type, as well as a low-area hardware technology such as stochastic computing (SC). We study the benefits and costs of applying SC to LSTM desig...