Deep Learning Accelerator in Loop Reliability Evaluation for Autonomous Driving
This addresses the problem of expensive and inefficient DLA design revisions for autonomous driving systems, though it appears incremental as it focuses on improving evaluation methods rather than a fundamental breakthrough.
The paper tackles the disconnect between low-level and high-level reliability metrics for deep learning accelerators (DLAs) in autonomous driving, proposing a DLA-in-loop platform to enable early system reliability evaluation and reduce costly design iterations.
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.