  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.
