research-article

Accelerating stochastic‐based reliability estimation for combinational circuits at RTL using GPU parallel computing

Authors:

Jungang LouAuthors Info & Claims

International Journal of Intelligent Systems, Volume 37, Issue 11

Pages 8309 - 8326

https://doi.org/10.1002/int.22940

Published: 26 September 2022 Publication History

Abstract

Reliable circuits help prevent artificial intelligence (AI) systems from being corrupted by the soft errors occurred in memories or combinational circuits, which promotes the development of AI security. However, it is a great challenge to measure the reliability of combinational circuits at register transfer level (RTL) rapidly and efficiently. In this paper, a new fast and accurate computational model based on stochastic computation (SC) is presented to meet these objectives. In the proposed approach, the circuit netlists at RTL are parsed to satisfy the requirements of SC on the bitstream structure of the circuits, and then a Sobol sequence‐based algorithm for generating uniform non‐Bernoulli sequences is built to reduce the random fluctuations occurred in probability calculations. After that, an adaptive algorithm based on a MAX–MIN ant system is constructed using graphics processing unit‐based parallel schemes to greatly accelerate the calculation. The experimental results validate our proposed technique, showing that this approach was approximately 51 and 42 times faster than the traditional SC approach and the stochastic computational model (SCM), respectively; its required sequence length was approximately 1.66 times shorter than that of the traditional SC approach, and its relative error was two times smaller than that of the SCM.

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Cited By

Shi ZXiao JJiang JZhang YZhou Y(2024)ARA-RCIV: Identifying Reliability-Critical Input Vectors of Logic Circuits Based on the Association Rules Analysis ApproachIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.337246143:8(2479-2492)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TCAD.2024.3372461
Shi ZXiao JZhu WJiang J(2024)A Reliability-Critical Path Identifying Method With Local and Global Adjacency Probability Matrix in Combinational CircuitsIEEE Transactions on Computers10.1109/TC.2023.332377273:1(123-137)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TC.2023.3323772

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Published In

cover image International Journal of Intelligent Systems

International Journal of Intelligent Systems Volume 37, Issue 11

November 2022

1841 pages

ISSN:0884-8173

DOI:10.1002/int.v37.11

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© 2022 Wiley Periodicals LLC.

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John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 26 September 2022

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Cited By

Shi ZXiao JJiang JZhang YZhou Y(2024)ARA-RCIV: Identifying Reliability-Critical Input Vectors of Logic Circuits Based on the Association Rules Analysis ApproachIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.337246143:8(2479-2492)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TCAD.2024.3372461
Shi ZXiao JZhu WJiang J(2024)A Reliability-Critical Path Identifying Method With Local and Global Adjacency Probability Matrix in Combinational CircuitsIEEE Transactions on Computers10.1109/TC.2023.332377273:1(123-137)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TC.2023.3323772

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