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