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The reliability of deep learning accelerators (DLAs) used in autonomousdriving systems has significant impact on the system safety. However, the DLAreliability is usually evaluated with low-level metrics like mean square errorsof the output which remains rather different from the high-level metrics liketotal distance traveled before failure in autonomous driving. As a result, thehigh-level reliability metrics evaluated at the post-silicon stage may stilllead to DLA design revision and result in expensive reliable DLA designiterations targeting at autonomous driving. To address the problem, we proposeda DLA-in-loop reliability evaluation platform to enable system reliabilityevaluation at the early DLA design stage.

Deep Learning Accelerator in Loop Reliability Evaluation for Autonomous Driving