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Title: CORLD: In-Stream Correlation Manipulation for Low-Discrepancy Stochastic Computing
Stochastic computing (SC) is a re-emerging computing paradigm providing low-cost and noise-tolerant designs for a wide range of arithmetic operations. SC circuits operate on uniform bit-streams with the value determined by the probability of observing 1’s in the bit-stream. The accuracy of SC operations highly depends on the correlation between input bit-streams. While some operations such as minimum and maximum value functions require highly correlated inputs, some other such as multiplication operation need uncorrelated or independent inputs for accurate computation. Developing low-cost and accurate correlation manipulation circuits is an important research in SC as these circuits can manage correlation between bit-streams without expensive bit-stream regeneration. This work proposes a novel in-stream correlator and decorrelator circuit that manages 1) correlation between stochastic bit-streams, and 2) distribution of 1’s in the output bit-streams. Compared to state-of-the-art solutions, our designs achieve lower hardware cost and higher accuracy. The output bit-streams enjoy a low-discrepancy distribution of bits which leads to higher quality of results. The effectiveness of the proposed circuits is shown with two case studies: SC design of sorting and median filtering  more » « less
Award ID(s):
2019511
NSF-PAR ID:
10338290
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
CORLD: In-Stream Correlation Manipulation for Low-Discrepancy Stochastic Computing," Proceedings of 40th IEEE/ACM International Conference on Computer-Aided Design (ICCAD)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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