SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection

• DOI: 10.48550/arxiv.1906.10327
• Publication Date: 2019-01-01
• Authors: Xiaofan Zhang; Cong Hao; Haoming Lu; Jiachen Li; Yuhong Li; Yuchen Fan; Kyle Rupnow; Jinjun Xiong; Thomas S. Huang; Humphrey Shi; Wen‐mei Hwu; Deming Chen
• Keywords: CONTEST; Edge device

Developing artificial intelligence (AI) at the edge is always challenging, since devices have limited computation capability and memory resources but need to meet demanding requirements, such as real-time processing, high throughput performance, inference accuracy. To overcome these challenges, we propose SkyNet, an extremely lightweight DNN with 12 convolutional (Conv) layers only 1.82 megabyte (MB) of parameters following a bottom-up design approach. SkyNet demonstrated in 56th IEEE/ACM Design Automation Conference System Contest (DAC-SDC), low power object detection challenge images captured by unmanned aerial vehicles (UAVs). won first place award for both GPU FPGA tracks contest: deliver 0.731 Intersection over Union (IoU) 67.33 frames per second (FPS) on TX2 0.716 IoU 25.05 FPS Ultra96 FPGA.