SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection
This addresses the problem of efficient object detection on low-power edge devices like UAVs, representing an incremental improvement in lightweight model design.
The paper tackles the challenge of deploying AI on edge devices with limited resources by proposing SkyNet, an extremely lightweight DNN with 1.82 MB parameters, which won first place in the DAC-SDC contest by achieving 0.731 IoU and 67.33 FPS on a TX2 GPU and 0.716 IoU and 25.05 FPS on an Ultra96 FPGA.
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.