The Correspondence Between Deterministic and Stochastic Digital Neurons: Analysis and Methodology

This paper analyzes the criteria for the direct correspondence between a deterministic neural network and its stochastic counterpart, and presents the guidelines that have been derived to establish such a correspondence during the design of a neural network application. In particular, the role of the slope and bias of the neuron activation function and that of the noise of its output have been addressed, thus filling a specific literature gap. This paper presents the results that have been theoretically derived in this regard, together with the simulations of few relevant application examples that have been performed to support them.