Improving Speed of MUX-FSM-based Stochastic Computing for On-device Neural Networks Jongsung Kang and Taewhan Kim

Kang, J.; Kim, T.

doi:10.5626/jcse.2022.16.2.79

Improving Speed of MUX-FSM-based Stochastic Computing for On-device Neural Networks Jongsung Kang and Taewhan Kim

Authors: Kang, J., Kim, T.

Journal: Journal of Computer Science Review and Engineering (JCSRE), ISSN 1694-4488

Citation: JCSRE 4(1), 2022-08-12.

DOI: 10.5626/jcse.2022.16.2.79

Type: Original Research

Abstract

This paper proposes an improved technique for implementing MUX-FSM-based stochastic computing (SC) in on-device neural networks. The proposed method focuses on enhancing the speed of SC operations, which is crucial for real-time applications on resource-constrained devices. By optimizing the MUX-FSM design, the authors aim to reduce latency and improve the overall efficiency of SC-based neural network accelerators.

Keywords

Stochastic Computing (SC), MUX-FSM, On-device Neural Networks, Hardware Acceleration, Low-power AI

Published by Academic Ink Review Journal. Open Access under CC BY 4.0.