An Energy-Efficient Quad-Camera Visual System for Autonomous Machines on FPGA Platform
This addresses the critical localization task for autonomous machines, offering significant energy and speed improvements, though it is incremental as it builds on existing ORB methods with hardware optimizations.
The paper tackles the performance and energy bottleneck of visual frontend localization in autonomous machines by designing an energy-efficient FPGA-based hardware architecture for ORB-based localization, achieving 5.6x speedup and 3.0x power reduction compared to Nvidia TX1, and 3.4x speedup and 34.6x power reduction compared to Intel i7.
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.