標題: Domain-Specific Approximation for Object Detection
作者: Chin, Ting-Wu
Yu, Chia-Lin
Halpern, Matthew
Genc, Hasan
Tsao, Shiao-Li
Reddi, Vijay Janapa
交大名義發表
資訊工程學系
National Chiao Tung University
Department of Computer Science
公開日期: 1-Jan-2018
摘要: There is growing interest in object detection in advanced driver assistance systems and autonomous robots and vehicles. To enable such innovative systems, we need faster object detection. In this article, we investigate the trade-off between accuracy and speed with domain-specific approximations (DSAs) for two state-of-the-art deep learning-based object detection meta-architectures. We study the effectiveness of applying approximation both statically and dynamically to understand their potential and applicability. By conducting experiments on the ImageNet VID dataset, we show that DSA has great potential to improve the speed of the system without deteriorating the accuracy of object detectors. To this end, we present our insights on harvesting DSA and devise a proof-of-concept runtime, AutoFocus, that exploits dynamic DSA.
URI: http://hdl.handle.net/11536/144613
ISSN: 0272-1732
期刊: IEEE MICRO
Volume: 38
起始頁: 31
結束頁: 40
Appears in Collections:Articles