Archytas: A Framework for Synthesizing and Dynamically Optimizing Accelerators for Robotic Localization

Weizhuang Liu, Bo Yu, Yiming Gan, Qiang Liu, Jie Tang, Shaoshan Liu, Yuhao Zhu

Abstract: Despite many recent efforts, accelerating robotic computing is still fundamentally challenging for two reasons. First, robotics software stack is extremely complicated. Manually designing an accelerator while meeting the latency, power, and resource specifications is unscalable. Second, the environment in which an autonomous machine operates constantly changes; a static accelerator design leads to wasteful computation. This paper takes a first step in tackling these two challenges using localization as a case study. We describe, a framework that automatically generates a synthesizable accelerator from the high-level algorithm description while meeting design constraints. The accelerator continuously optimizes itself at run time according to the operating environment to save power while sustaining performance and accuracy. is able to generate FPGA-based accelerator designs that cover large a design space and achieve orders of magnitude performance improvement and/or energy savings compared to state-of-the-art baselines.