Efficient suspect selection in unreachable state diagnosis

Efficient suspect selection in unreachable state diagnosis In the modern hardware design cycle, correcting the design when verification reveals a state to be erroneously unreachable can be a time-consuming manual process. Recently-developed algorithms aid the engineer in finding the root cause of the failure in these cases. However, they exhaustively examine every design location to determine a set of possible root causes, potentially requiring substantial runtime. This work develops a novel approach that is applicable to practical diagnosis problems. In contrast to previous approaches, it considers only a portion of the design locations but still finds the complete solution set to the problem. The presented approach proceeds through a series of iterations, each considering a strategically-chosen subset of the design locations (a suspect set) to determine if they are root causes. The results of each iteration inform the choice of suspect set for the next iteration. By choosing the first iteration’s suspect set appropriately, the algorithm is able to find the complete solution set to the problem. Empirical results on industrial designs and standard benchmark designs demonstrate a 15x speedup compared to the previous approach, while considering only 18.7% of the design locations as suspects. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

Efficient suspect selection in unreachable state diagnosis

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer International Publishing AG, part of Springer Nature
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mathematics, general; Computer Science, general; Complex Systems
ISSN
1012-2443
eISSN
1573-7470
D.O.I.
10.1007/s10472-018-9578-x
Publisher site
See Article on Publisher Site

Abstract

In the modern hardware design cycle, correcting the design when verification reveals a state to be erroneously unreachable can be a time-consuming manual process. Recently-developed algorithms aid the engineer in finding the root cause of the failure in these cases. However, they exhaustively examine every design location to determine a set of possible root causes, potentially requiring substantial runtime. This work develops a novel approach that is applicable to practical diagnosis problems. In contrast to previous approaches, it considers only a portion of the design locations but still finds the complete solution set to the problem. The presented approach proceeds through a series of iterations, each considering a strategically-chosen subset of the design locations (a suspect set) to determine if they are root causes. The results of each iteration inform the choice of suspect set for the next iteration. By choosing the first iteration’s suspect set appropriately, the algorithm is able to find the complete solution set to the problem. Empirical results on industrial designs and standard benchmark designs demonstrate a 15x speedup compared to the previous approach, while considering only 18.7% of the design locations as suspects.

Journal

Annals of Mathematics and Artificial IntelligenceSpringer Journals

Published: Mar 13, 2018

References

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