用FPGA加速采样控制，让机器人更快更省电。
Real-Time, Energy-Efficient, Sampling-Based Optimal Control via FPGA Acceleration
- FPGA优化MPPI算法，深度流水线化消除同步瓶颈
- 比嵌入式GPU/CPU快3.1至7.5倍，能耗降低2.5至5.4倍
- 适合电池受限的边缘机器人实时控制场景
自主移动机器人（AMRs）在搜救与远程探测中需快速可靠的规划与控制。基于模型预测路径积分控制（MPPI）的采样方法虽高效且天然适配GPU加速，但在功耗受限的嵌入式平台中，其CPU/GPU实现常难以满足严苛的延迟与能耗要求。为此，本文提出一种面向FPGA的MPPI优化设计，通过细粒度并行与算法阶段间并行，消除同步瓶颈。该方案在平均性能上相较嵌入式GPU和CPU优化实现分别提升3.1倍至7.5倍，同时能耗降低2.5倍至5.4倍。结果表明，FPGA架构是边缘机器人高能效、高性能控制的可行方向。
原文摘要 · Abstract (English)
Autonomous mobile robots (AMRs), used for search-and-rescue and remote exploration, require fast and robust planning and control schemes. Sampling-based approaches for Model Predictive Control, especially approaches based on the Model Predictive Path Integral Control (MPPI) algorithm, have recently proven both to be highly effective for such applications and to map naturally to GPUs for hardware acceleration. However, both GPU and CPU implementations of such algorithms can struggle to meet tight energy and latency budgets on battery-constrained AMR platforms that leverage embedded compute. To address this issue, we present an FPGA-optimized MPPI design that exposes fine-grained parallelism and eliminates synchronization bottlenecks via deep pipelining and parallelism across algorithmic stages. This results in an average 3.1x to 7.5x speedup over optimized implementations on an embedded GPU and CPU, respectively, while simultaneously achieving a 2.5x to 5.4x reduction in energy usage. These results demonstrate that FPGA architectures are a promising direction for energy-efficient and high-performance edge robotics.
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