Nowadays, General Purpose Graphics Processing Units (GPGPUs) devices are considered as promising solutions for high-performance safety-critical applications, such as those in the automotive field. However, their adoption requires solutions to effectively detect faults arising in the device during the operative life. Hence, effective in-field test solutions are required to guarantee high-reliability levels. In this paper, we leverage the results of Software-Based Self-Test (SBST) based approaches for GPGPUs by deploying new techniques for automating the identification of untestable faults (UF). Our methodology has achieved fault coverage of 82.8% when applied to an open-source implementation of the NVIDIA G80 GPU architecture. The proposed approach combining SBSTs and UFs identification appears as an effective solution for the reliability analysis of GPGPUs.

Untestable faults identification in GPGPUs for safety-critical applications / Condia, Josie E. Rodriguez; Da Silva, Felipe A.; Hamdioui, S.; Sauer, C.; Reorda, M. Sonza. - ELETTRONICO. - (2019), pp. 570-573. ((Intervento presentato al convegno 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS) tenutosi a Genova nel 27-29 Nov. 2019 [10.1109/ICECS46596.2019.8964677].

Untestable faults identification in GPGPUs for safety-critical applications

Condia, Josie E. Rodriguez;Hamdioui, S.;Reorda, M. Sonza
2019

Abstract

Nowadays, General Purpose Graphics Processing Units (GPGPUs) devices are considered as promising solutions for high-performance safety-critical applications, such as those in the automotive field. However, their adoption requires solutions to effectively detect faults arising in the device during the operative life. Hence, effective in-field test solutions are required to guarantee high-reliability levels. In this paper, we leverage the results of Software-Based Self-Test (SBST) based approaches for GPGPUs by deploying new techniques for automating the identification of untestable faults (UF). Our methodology has achieved fault coverage of 82.8% when applied to an open-source implementation of the NVIDIA G80 GPU architecture. The proposed approach combining SBSTs and UFs identification appears as an effective solution for the reliability analysis of GPGPUs.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2785749