تقرير
Deep Learning Accelerator in Loop Reliability Evaluation for Autonomous Driving
| العنوان: | Deep Learning Accelerator in Loop Reliability Evaluation for Autonomous Driving |
|---|---|
| المؤلفون: | Huang, Haitong, Liu, Cheng |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Artificial Intelligence, Computer Science - Hardware Architecture, Computer Science - Robotics |
| الوصف: | 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. Comment: 2 pages, 2 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2306.11759 |
| رقم الأكسشن: | edsarx.2306.11759 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |