arrow
Return

A Fast Converging Normalization Unit for Stochastic Computing

delete2018-04-01
delete6
PRE
AI
K
Kaining Han *
J
Jianhao Hu
J
Jienan Chen
Z
Zhengbing Zhang
H
Hao Lü
DOI:10.1109/TCSII.2017.2735180delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Stochastic computing is a promising technology for low hardware cost and low power digital signal processing (DSP) systems. However, the quite slow convergence of stochastic normalization leads to low throughput of stochastic computing based DSP systems. In this brief, we propose a fast converging stochastic normalization unit based on joint probability tracking (JPT) method, which operates modified probability tracking on random bit streams to perform the normalization. Simulation results show that the convergence speed of the JPT scheme is as 3.5 times as that of existing methods. According to the synthesis results on 65-nm CMOS technology, the JPT scheme achieves higher (2.8 times) hardware efficiency gains with respect to the existing stochastic computing-based schemes.
Keywords:
Stochastic computing
stochastic logic
normalization
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

I
IEEE Transactions on Circuits and Systems and Express Briefs
IF:
4.9
Papers:
8.8K
Citations:
2.5W

Organization

No organization information available