针对嵌入式系统优化实时最优控制，提升机器人计算效率。
Characterizing and Optimizing Real-Time Optimal Control for Embedded SoCs
- 对比CPU、向量处理器与专用加速器，找到最佳硬件架构
- 实现3.71倍速度提升，系统功耗降低27%
- 提出代码生成流程，简化算法部署复杂度
资源受限的机器人在执行如运动控制和操作等计算密集型任务时面临挑战，尤其对于模型预测控制（MPC）这类实时最优控制算法。本文通过全面的设计空间探索，识别适用于此类模型驱动控制算法的最佳硬件计算架构。我们对通用标量CPU、向量处理器及专用加速器等代表性设计进行性能分析与优化。基于内核级基准测试与端到端机器人场景评估，包括在一款自研的RISC-V多核向量SoC上的硬件在环测试，量化比较了不同架构设计点在性能、面积和利用率方面的表现。结果表明，针对性的架构改进结合深层软硬件协同优化，可使MPC实现最高3.71倍的速度提升，系统级功耗降低达27%，同时完成机器人任务。最后，我们提出一种代码生成流程，以降低将机器人工作负载映射到专用架构所需的复杂工程成本。
原文摘要 · Abstract (English)
Resource-limited robots face significant challenges in executing computationally intensive tasks, such as locomotion and manipulation, particularly for real-time optimal control algorithms like Model Predictive Control (MPC). This paper provides a comprehensive design space exploration to identify optimal hardware computation architectures for these demanding model-based control algorithms. We profile and optimize representative architectural designs, including general-purpose scalar CPUs, vector processors, and specialized accelerators. By characterizing kernel-level benchmarks and end-to-end robotic scenarios, including a hardware-in-the-loop evaluation on a fabricated RISC-V multi-core vector SoC, we present a quantitative comparison of performance, area, and utilization across distinct architectural design points. Our findings demonstrate that targeted architectural modifications, coupled with deep software and system optimizations, enable up to 3.71x speedups for MPC, resulting in up to 27% system-level power reductions while completing robotic tasks. Finally, we propose a code generation flow designed to simplify the complex engineering effort required for mapping robotic workloads onto specialized architectures.
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