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Parallel Software-Based Self-Testing with Bounded Model Checking for Kilo-Core Networks-on-Chip
Tongji Univ, Peoples R China.
Tongji Univ, Peoples R China.
Tongji Univ, Peoples R China.
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
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2023 (English)In: Journal of Computer Science and Technology, ISSN 1000-9000, E-ISSN 1860-4749, Vol. 38, no 2, p. 405-421Article in journal (Refereed) Published
Abstract [en]

Online testing is critical to ensuring reliable operations of the next generation of supercomputers based on a kilo-core network-on-chip (NoC) interconnection fabric. We present a parallel software-based self-testing (SBST) solution that makes use of the bounded model checking (BMC) technique to generate test sequences and parallel packets. In this method, the parallel SBST with BMC derives the leading sequence for each routers internal function and detects all functionally- testable faults related to the function. A Monte-Carlo simulation algorithm is then used to search for the approximately optimum configuration of the parallel packets, which guarantees the test quality and minimizes the test cost. Finally, a multi-threading technology is used to ensure that the Monte-Carlo simulation can reach the approximately optimum configuration in a large random space and reduce the generating time of the parallel test. Experimental results show that the proposed method achieves a high fault coverage with a reduced test overhead. Moreover, by performing online testing in the functional mode with SBST, it effectively avoids the over-testing problem caused by functionally untestable turns in kilo-core NoCs.

Place, publisher, year, edition, pages
SPRINGER SINGAPORE PTE LTD , 2023. Vol. 38, no 2, p. 405-421
Keywords [en]
software-based self-testing (SBST); parallel test; kilo-core networks-on-chip (NoCs); online testing
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-196923DOI: 10.1007/s11390-022-2553-3ISI: 001017832400013OAI: oai:DiVA.org:liu-196923DiVA, id: diva2:1792082
Note

Funding Agencies|National Key Research and Development Program of China [2020YFB1600201]; National Natural Science Foundation of China (NSFC) [61974105, 62090024, U20A20202]; Zhejiang Lab [2021KC0AB01]

Available from: 2023-08-28 Created: 2023-08-28 Last updated: 2023-08-28

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  • apa
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  • de-DE
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