A Stochastic Computing Sigma-Delta Adder Architecture for Efficient Neural Network Design

Abstract

A stochastic computing (SC) adder architecture, based on sigma-delta modulation is introduced. The operation principle of the stochastic computing sigma-delta (SCSD) adder is presented and the adder is modeled using Markov Chains resulting in the derivation of output’s first-moment statistics and the demonstration of its fast-convergence. The SCSD’s single-bit output enables the connection to existing Stochastic Finite-State Machines realizing non-linear functions and the formation of cascade processing structures appropriate for efficient realizations of SC artificial neurons. To demonstrate the proposed adder’s efficacy, a SCSD adder-based neuron was designed and used as a building block of a SCSD Multi-Layer Perceptron (MLP). The computational accuracy of the SCSD MLP and the hardware resources it requires are compared to those of the Fixed-Point arithmetic implementation, highlighting the effectiveness and significant area savings of the proposed SC approach. Furthermore, the accuracy and hardware requirements of the proposed SCSD MLP are also compared to those of corresponding SC architectures in the literature.