An Energy Efficient and Runtime Reconfigurable Accelerator for Robotic Localization

Abstract

Accurate and efficient localization of robots under limited on-board resources has fueled specialized localization accelerators. Despite many recent efforts, accelerating robotic localization is still fundamentally challenging. To tackle the challenges, the paper proposes a configurable hardware architecture and a design space optimization method to automatically generate an optimal accelerator design under the design constraints. Data locality, sparsity, and fixed-point arithmetic optimization techniques that are specific to the localization algorithm are exploited to customize the accelerator. In addition, a low-cost runtime configuration mechanism is proposed to enable the accelerator to continuously optimize itself at runtime according to the operating environment to save power while sustaining performance and accuracy. The evaluation on FPGA demonstrates that the proposed accelerator achieves orders of magnitude performance improvement and/or energy savings compared to the software implementation on Intel and Arm CPUs; and substantially outperforms existing FPGA accelerators in terms of performance and energy.