Abstract

The applications of the Artificial Intelligence are currently dominating the technology landscape. Meanwhile, the conventional Von Neumann architectures are struggling with the data-movement bottleneck to meet the ever-increasing performance demands of these data-centric applications. Moreover, The vector-matrix multiplication cost, in the binary domain, is a major computational bottleneck for these applications. This paper introduces a novel digital in-memory stochastic computing architecture that leverages the simplicity of the stochastic computing for in-memory vector-matrix multiplication. The proposed architecture incorporates several new approaches including a new stochastic number generator with ideal binary-to-stochastic mapping, a best seeding approach for accurate-enough low stochastic bit-precisions, a hybrid stochastic-binary accumulation approach for vector-matrix multiplication, and the conversion of conventional memory read operations into on-the-fly stochastic multiplication operations with negligible overhead. Thanks to the combination of these approaches, the accuracy analysis of the vector-matrix multiplication benchmark shows that scaling down the stochastic bit-precision from 16-bit to 4-bit achieves nearly the same average error (less than 3%). The derived analytical model of the proposed in-memory stochastic computing architecture demonstrates that the 4-bit stochastic architecture achieves the highest throughput per sub-array (122 Ops/Cycle), which is better than the 16-bit stochastic precision by 4.36x, while still maintaining a small average error of 2.25%.