Edge-based and autonomous, deep learning computer vision applications, such as those used in surveillance or traffic management, must be assuredly correct and performant. However, realizing these applications in practice incurs a number of challenges. First, the constraints on edge resources precludes the use of large-sized, deep learning computer vision models. Second, the heterogeneity in edge resource types causes different execution speeds and energy consumption during model inference.

2022 IEEE International Conference on Assured Autonomy (ICAA)

IEEE

978-1-6654-8539-5

10.1109/ICAA52185.2022.00021