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 **SkyNet is published as a conference paper at [Conference on Machine Learning and Systems (MLSys)](https://mlsys.org/Conferences/2020/Schedule?type=Oral).**
 
-This is a repository for SkyNet, a hardware-efficient method specialized in object detection and tracking. SkyNet is demonstrated in [the 56th IEEE/ACM Design Automation Conference System Design Contest (DAC-SDC)](http://www.cse.cuhk.edu.hk/~byu/2019-DAC-SDC/index.html), a low power object detection challenge in images captured by unmanned aerial vehicles (UAVs). SkyNet won the first place award for both 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.
+This is a repository for SkyNet, a new hardware-efficient DNN specialized in object detection and tracking. SkyNet is developed based on the SkyNet design methodology to facilitate edge AI solutions, and demonstrated in [the 56th IEEE/ACM Design Automation Conference System Design Contest (DAC-SDC)](http://www.cse.cuhk.edu.hk/~byu/2019-DAC-SDC/index.html), a low power object detection challenge for real-life unmanned aerial vehicle (UAV) applications. SkyNet won the first place award for both 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.
 
 The GPU team (iSmart3-SkyNet) members are: Xiaofan Zhang*, Haoming Lu*, Jiachen Li, Cong Hao, Yuchen Fan, Yuhong
 Li, Sitao Huang, Bowen Cheng, Yunchao Wei, Thomas Huang, Jinjun Xiong, Honghui Shi, Wen-mei Hwu, Deming Chen.