A System-Level Solution for Low-Power Object Detection | |
Li, Fanrong2,3; Mo, Zitao2,3; Wang, Peisong3; Liu, Zejian2,3; Zhang, Jiayun3; Li, Gang2,3; Hu, Qinghao3; He, Xiangyu2,3; Leng, Cong1,3; Zhang, Yang1,3 | |
2019 | |
会议日期 | 2019 |
会议地点 | Seoul, Korea |
英文摘要 | Object detection has made impressive progress in recent years with the help of deep learning. However, state-of-the-art algorithms are both computation and memory in- tensive. Though many lightweight networks are developed for a trade-off between accuracy and efficiency, it is still a challenge to make it practical on an embedded device. In this paper, we present a system-level solution for efficient object detection on a heterogeneous embedded device. The detection network is quantized to low bits and allows efficient implementation with shift operators. In order to make the most of the benefits of low-bit quantization, we design a dedicated accelerator with programmable logic. Inside the accelerator, a hybrid dataflow is exploited according to the heterogeneous property of different convolutional layers. We adopt a straightforward but resource-friendly column-prior tiling strategy to map the computation-intensive convolutional layers to the accelerator that can support arbitrary feature size. Other operations can be performed on the low-power CPU cores, and the entire system is executed in a pipelined manner. As a case study, we evaluate our object detection system on a real-world surveillance video with input size of 512×512, and it turns out that the system can achieve an inference speed of 18 fps at the cost of 6.9W (with display) with an mAP of 66.4 verified on the PASCAL VOC 2012 dataset. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48623] |
专题 | 类脑芯片与系统研究 |
通讯作者 | Cheng, Jian |
作者单位 | 1.AiRiA 2.University of Chinese Academy of Sciences 3.Institute of Automation, Chinese Academy of Sciences 4.CAS Center for Excellence in Brain Science and Intelligence Technology |
推荐引用方式 GB/T 7714 | Li, Fanrong,Mo, Zitao,Wang, Peisong,et al. A System-Level Solution for Low-Power Object Detection[C]. 见:. Seoul, Korea. 2019. |
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