LOCATOR: Low-power ORB accelerator for autonomous cars

Simultaneous Localization And Mapping (SLAM) is crucial for autonomous navigation. ORB-SLAM is a state-of-the-art Visual SLAM system based on cameras used for self-driving cars. In this paper, we propose a high-performance, energy-efficient, and functionally accurate hardware accelerator for ORB SLAM, focusing on its most time-consuming stage: Oriented FAST and Rotated BRIEF (ORB) feature extraction. The Rotated BRIEF (rBRIEF) descriptor generation is the main bottleneck in ORB computation, as it exhibits highly irregular access patterns to local on-chip memories causing a high-performance penalty due to bank conflicts. We introduce a technique to find an optimal static pattern to perform parallel accesses to banks based on a genetic algorithm. Furthermore, we propose the combination of an rBRIEF pixel duplication cache, selective ports replication, and pipelining to reduce latency without compromising cost. The accelerator achieves a reduction in energy consumption of 14597x and 9609x, with respect to high-end CPU and GPU platforms, respectively.(c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).