FPGA-based Power Efficient Face Detection for Mobile Robots

摘要

Autonomous mobile robots need perception module to understand nearby environments, avoid obstacles and, most importantly, interact with the humans in the dynamic environments. In order to perform human robot interaction for mobile robots, face detection is an inevitable and important function. In recent years, deep learning has emerged to be the most accurate approach for face detection problems. However, deep learning models have very high computational density, therefore cannot be executed in real-time on the embedded low-end processors that most of the robot systems equip with. Switching to high-end CPU or GPU generally means much higher cost and power consumption. Meanwhile, FPGA is well suited for robot application because it provides high computing power with low cost and high power efficiency. In this paper we design an FPGA based system for the acceleration of MTCNN, which is one of the most accurate face detection neural networks. We evaluate the system using a Zynq 706 board and compare the performance with embedded CPU and popular GPU edge computing platform Jetson TX2. Our FPGA system has 40× lower latency than Jetson TX2 with 2.5 × higher power efficiency, making it a promising candidate for egde computing on mobile robot applications.