Reviews: The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic

This paper proposes to use stochastic logic to approximate the activation function of LSTM. Binarization of non-linear units in deep neural networks is an interesting topic that can be relevant for low-resources computing. The main contribution of the paper was the application of stochastic logic to approximate activation functions(e.g. The authors applied the technique to a variant of LSTM model on PTB. Given that the technique is not really tied to the LSTM model, it would be more interesting to evaluate more model architectures(e.g.