Untestable faults identification in GPGPUs for safetycritical applications Nowadays, General Purpose Graphics Processing Units (GPGPUs) devices are considered as promising solutions for highperformance safetycritical 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 infield test solutions are required to guarantee highreliability levels. In this paper, we leverage the results of SoftwareBased SelfTest (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 opensource 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.

Evaluating the Impact of Accuracy Relaxation in the Reliability of GPU Register Files Thanks to the high computing power, GPUs have joined application domains where reliability is a major concern. Faults on electronic components are mainly caused by energized particles, which may cause malfunction and make them result in incorrect output. Errors may be unacceptable for most applications but a small margin of error can be considered safe in some cases. This work uses an approximate computing perspective to analyze the influence of application accuracy relaxation in GPU register files reliability. We perform a fault injection campaign in a Kepler GPU to identify registersu0027 vulnerability and the impact on resulting data. Results show increase in register file reliability in an average of 71.6% for 1% of application accuracy relaxation.