In our past few years' of commercial deployment experiences, we identify localization as a critical task in autonomous machine applications, and a great acceleration target. In this paper, based on the observation that the visual frontend is a major performance and energy consumption bottleneck, we present our design and implementation of an energy-efficient hardware architecture for ORB (Oriented-Fast and Rotated- BRIEF) based localization system on FPGAs. To support our multi-sensor autonomous machine localization system, we present hardware synchronization, frame-multiplexing, and parallelization techniques, which are integrated in our design. Compared to Nvidia TX1 and Intel i7, our FPGA-based implementation achieves 5.6x and 3.4x speedup, as well as 3.0x and 34.6x power reduction, respectively.
@article{arxiv.2104.00192,
title = {An Energy-Efficient Quad-Camera Visual System for Autonomous Machines on FPGA Platform},
author = {Zishen Wan and Yuyang Zhang and Arijit Raychowdhury and Bo Yu and Yanjun Zhang and Shaoshan Liu},
journal= {arXiv preprint arXiv:2104.00192},
year = {2021}
}
Comments
To appear in IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), June 6-9, 2021, Virtual