Abstract
Stochastic computing provides low-area and fault- tolerant computing circuits. However, the required stochastic number generators (SNGs) in these circuits are area consuming and can diminish their overall saving in hardware size, particularly if several SNGs are required. A SNG circuit consists of two parts: a random number source (RNS), e.g., a linear feedback shift register (LFSR), and a probability converter circuit (PCC), e.g., a comparator. In this paper, we propose area-efficient SNGs by sharing the permuted output of one RNS among several SNGs. With no hardware overhead, the proposed architecture generates random bit streams with minimum stochastic computing correlation (SCC). Compared to the circular shifting approach presented in recent prior work, our approach produces stochastic bit streams with 52% and 67% less average SCC when a 8-bit and a 10-bit LFSR are shared between two SNGs, respectively. We evaluated the proposed method for several applications. The results show that, compared to prior work, our approach yields lower MSE values with the same (or even lower) area-cost.
Original language | English |
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Title of host publication | GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings |
ISBN (Electronic) | 9781728127231 |
DOIs | |
State | Published - Nov 2019 |
Event | 7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019 - Ottawa, Canada Duration: Nov 11 2019 → Nov 14 2019 |
Publication series
Name | GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings |
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Conference
Conference | 7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019 |
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Country/Territory | Canada |
City | Ottawa |
Period | 11/11/19 → 11/14/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- Accuracy control
- Random number generator
- Resource sharing
- Stochastic computing
ASJC Scopus subject areas
- Information Systems
- Information Systems and Management
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Signal Processing