论文标题
一种用于机器人定位系统的基于FPGA的节能且可运行的可配置的加速器
An Energy-Efficient and Runtime-Reconfigurable FPGA-Based Accelerator for Robotic Localization Systems
论文作者
论文摘要
同时定位和映射（SLAM）估计了代理的轨迹和构造图，并且本地化是所有计算机的自主机器中的基本内核，从无人机，AR，VR到自动驾驶汽车。在这项工作中，我们提出了一种用于机器人定位的基于FPGA的能源效率和运行时可配置的加速器。我们利用特定的数据局部性，稀疏性，重复使用和并行性，并在最新的情况下实现> 5倍的性能改善。尤其是，根据环境，我们的设计在运行时可以重新配置，以节省功率，同时持续准确性和性能。
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.
