An Embedded Tracking System with Neural Network Accelerator

With robots and unmanned aerial vehicles (UAVs) being more and more employed in real-life scenarios for monitoring and surveillance, there is a increasing demand for deploying various video processing applications in mobile systems. However, with limited on-board computational resources and power consumption, the application in this domain requires that the tracking platforms equipped should have outstanding computing power to handle the tasks in real-time with high-accuracy, while at the same time, fit the highly constrained environment of small size, light weight, and low power consumption (SWaP) for the purpose of long-term surveillance. In this paper, we proposed a new autonomous object tracking system based on an embedded platform, leveraging the emerging neural network hardware which is capable of massive parallel pattern recognition processing and demands only a low level power consumption. Further, a prototype of the tracking system that combines a low-power neural network chip, CogniMem, and an embedded development board, BeagleBone, is developed. Our experimental results show that the power consumption for the entire system is only about 2. 25W, which signifies a promising future of applying ultra-low-power neuromorphic hardware as a accelerator in recognition tasks.