Statues: Energy-Efficient Video Object Detection on Edge Security Devices with Computational Skipping

초록

This paper proposes a software and hardware co-optimization method tailored to object detection on edge security devices. Object detection on video inputs requires a massive number of MAC computations, so it is difficult to implement a real-time inference task with limited computational budget on edge security devices. To relieve such computational complexity, we propose a Statues, selective computational skipping approach by scoring pixel differences in the recent inputs. If the score is low enough, we skip the rest DNN part because we expect the almost same object detection result as the previous frame. Our Statues approach maximizes energy-efficiency by 44% compared with the conventional method with negligible accuracy drop.

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