Deterministic Methods for Stochastic Computing using Low-Discrepancy Sequences

Recently, deterministic approaches to stochastic computing (SC) have been proposed. These compute with the same constructs as stochastic computing but operate on deterministic bit streams. These approaches reduce the area, greatly reduce the latency (by an exponential factor), and produce completely...

Full description

Saved in:
Bibliographic Details
Published in2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) pp. 1 - 8
Main Authors Najafi, M. Hassan, Lilja, David J., Riedel, Marc
Format Conference Proceeding
LanguageEnglish
Published ACM 01.11.2018
Subjects
Clocks
Convergence
Correlation
deterministic computing
fast converging process
Fluctuations
Generators
Hardware
Logic gates
low-discrepancy sequences
scalable design
Stochastic computing
Online AccessGet full text

Cover

More Information
Summary:Recently, deterministic approaches to stochastic computing (SC) have been proposed. These compute with the same constructs as stochastic computing but operate on deterministic bit streams. These approaches reduce the area, greatly reduce the latency (by an exponential factor), and produce completely accurate results. However, these methods do not scale well. Also, they lack the property of progressive precision enjoyed by SC. As a result, these deterministic approaches are not competitive for applications where some degree of inaccuracy can be tolerated. In this work we introduce two fast-converging, scalable deterministic approaches to SC based on low-discrepancy sequences. The results are completely accurate when running the operations for the required number of cycles. However, the computation can be truncated early if some inaccuracy is acceptable. Experimental results show that the proposed approaches significantly improve both the processing time and area-delay product compared to prior approaches.
ISSN:1558-2434
DOI:10.1145/3240765.3240797