A DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation

Abstract

This demonstration is related to the submitted paper, 'A 0.95 mJ/frame DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation', (Submission ID: 45). Lightweight DNN is essential for energy-efficient DNN execution in mobile/edge devices. However, it suffers from significant accuracy degradation when it is applied to the new environment. In other words, the lightweight DNN loses generality due to its low network capacity. In particular, the mobile-oriented DNNs do not work properly in unexpected situations such as camera malfunction as shown in Fig. 1. Therefore, accuracy compensation for unpredictable accidents is important to prevent critical system damage. Real-time online DNN tuning is a promising solution to compensate accuracy of the lightweight network while maintaining its hardware benefits. In this demonstration, we demonstrate online tuning-based lightweight object detection execution based on our proposed processor and systems. The proposed processor successfully demonstrates 46.6 FPS object detection with 0.95 mJ/frame energy consumption which is the state-of-the-art performance compared with the existing processors.