自动驾驶深度学习加速器在环可靠性评估
Deep Learning Accelerator in Loop Reliability Evaluation for Autonomous Driving
AI总结：
针对自动驾驶中DLA可靠性评估指标与系统级指标脱节的问题，提出DLA在环可靠性评估平台，在早期设计阶段实现系统级可靠性评估，以减少设计迭代成本。
摘要：
自动驾驶系统中使用的深度学习加速器（DLA）的可靠性对系统安全具有重大影响。然而，DLA可靠性通常使用输出均方误差等低级指标进行评估，这与自动驾驶中故障前总行驶距离等高级指标存在较大差异。因此，在硅后阶段评估的高级可靠性指标仍可能导致DLA设计修订，并导致针对自动驾驶的昂贵且可靠的DLA设计迭代。为解决这一问题，我们提出了一个DLA在环可靠性评估平台，以便在DLA设计早期阶段实现系统可靠性评估。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
