Simultaneous Localization and Mapping (SLAM) estimates agents' trajectories and constructs maps, and localization is a fundamental kernel in autonomous machines at all computing scales, from drones, AR, VR to self-driving cars. In this work, we present an energy-efficient and runtime-reconfigurable FPGA-based accelerator for robotic localization. We exploit SLAM-specific data locality, sparsity, reuse, and parallelism, and achieve >5x performance improvement over the state-of-the-art. Especially, our design is reconfigurable at runtime according to the environment to save power while sustaining accuracy and performance.
@article{arxiv.2202.08952,
title = {An Energy-Efficient and Runtime-Reconfigurable FPGA-Based Accelerator for Robotic Localization Systems},
author = {Qiang Liu and Zishen Wan and Bo Yu and Weizhuang Liu and Shaoshan Liu and Arijit Raychowdhury},
journal= {arXiv preprint arXiv:2202.08952},
year = {2022}
}
Comments
First three authors contributed equally. 2 pages, 6 figures, IEEE Custom Integrated Circuits Conference (CICC), April 24-27, 2022, Newport Beach, CA, USA