Hardware implementation of stochastic-based Neural Networks

In this work we review the basic principles of stochastic logic and its application to the hardware implementation of Neural Networks. In this paper we show the mathematical basis of stochastic-based neurons along with the specific circuits that are needed to implement the processing of each neuron. We also propose a new methodology to reproduce the non-linear activation function. The proposed methodology can be used to implement any kind of Neural Network.