High Quality Down-Sampling for Deterministic Approaches to Stochastic Computing

Abstract

Deterministic approaches to stochastic computing (SC) have been recently proposed to remove the random fluctuation and correlation problems of SC and so produce completely accurate results with stochastic logic. For many applications of SC, such as image processing and neural networks, completely accurate computation is not required for all input data. Decision-making on some input data can be done in a much shorter time using only a good approximation of the input values. While the deterministic approaches to SC are appealing by generating completely accurate results, the cost of precise results makes them energy inefficient for the cases when slight inaccuracy is acceptable. In this work, we propose a high quality down-sampling method for previously proposed deterministic approaches to SC by generating pseudo-random-but accurate-stochastic bit-stream. The result is a much better accuracy for a given number of input bits. Experimental results show that the processing time and the energy consumption of these deterministic methods are improved up to 61 and 41 percent, respectively, while allowing a mean absolute error (MAE) of 0.1 percent, and up to 500X and 334X improvement, respectively, for an MAE of 3.0 percent. The accuracy and the energy consumption are also improved compared to conventional random stream-based stochastic implementations.