Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding requirements, such as real-time processing, high throughput performance, and high inference accuracy. To overcome these challenges, we propose SkyNet, an extremely lightweight DNN with 12 convolutional (Conv) layers and only 1.82 megabyte (MB) of parameters following a bottom-up DNN design approach. SkyNet is demonstrated in the 56th IEEE/ACM Design Automation Conference System Design Contest (DAC-SDC), a low power object detection challenge in images captured by unmanned aerial vehicles (UAVs). SkyNet won the first place award for both the GPU and FPGA tracks of the contest: we deliver 0.731 Intersection over Union (IoU) and 67.33 frames per second (FPS) on a TX2 GPU and deliver 0.716 IoU and 25.05 FPS on an Ultra96 FPGA.
@article{arxiv.1906.10327,
title = {SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection},
author = {Xiaofan Zhang and Cong Hao and Haoming Lu and Jiachen Li and Yuhong Li and Yuchen Fan and Kyle Rupnow and Jinjun Xiong and Thomas Huang and Honghui Shi and Wen-mei Hwu and Deming Chen},
journal= {arXiv preprint arXiv:1906.10327},
year = {2019}
}