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arXiv · 2202.08952

An Energy-Efficient and Runtime-Reconfigurable FPGA-Based Accelerator for Robotic Localization Systems

Qiang Liu·Zishen Wan·Bo Yu·Weizhuang Liu·Shaoshan Liu·Arijit Raychowdhury
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Abstract

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.

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Qiang Liu, Zishen Wan, Bo Yu, Weizhuang Liu, Shaoshan Liu, Arijit Raychowdhury. 2022-02-18. An Energy-Efficient and Runtime-Reconfigurable FPGA-Based Accelerator for Robotic Localization Systems. https://arxiv.org/abs/2202.08952

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