APEX-RBD：面向硬件高效机器人动力学加速器设计的混合精度探索框架

中文摘要 AI 辅助

刚体动力学（RBD）是实时机器人控制的计算核心，但其巨大的计算复杂度形成了性能瓶颈，亟需专用硬件加速器。然而，这些加速器的硬件资源与功耗成本极高，难以部署在资源受限的边缘平台。量化虽为优化边缘计算RBD硬件提供了可行路径，但现有均匀精度方法因忽略不同变量的量化敏感性而效率低下。混合精度虽为更优选择，但其探索因搜索空间庞大、用于运动精度评估的闭环仿真成本过高而难以处理。为应对这些挑战，我们提出APEX-RBD，这一自动化框架可使混合精度探索在计算上易于处理，同时有效识别硬件高效的配置。具体而言，它通过变量分组与敏感性分析执行物理驱动的搜索空间剪枝，并采用数据高效、先验信息驱动的代理模型实现快速轨迹误差预测。该方法引导混合优化器在用户定义的精度与性能约束下，识别面积与功耗高效的设计。实验结果表明，在多种机器人平台上，与均匀精度基线相比，APEX-RBD发现的设计可实现最高1.9倍的面积缩减与1.8倍的功耗节省。

英文摘要

Rigid Body Dynamics (RBD) forms the computational core of real-time robotic control, but its immense computational complexity creates a performance bottleneck that necessitates dedicated hardware accelerators. However, the substantial hardware resource and power costs of these accelerators make their deployment on resource-constrained edge platforms highly challenging. While quantization offers a promising path to optimize RBD hardware for edge computing, existing uniform-precision approaches remain inefficient by ignoring the diverse quantization sensitivities of different variables. Although mixed-precision offers a superior alternative, its exploration is intractable due to a vast search space and the prohibitive cost of closed-loop simulation for motion accuracy evaluation. To address these challenges, we introduce APEX-RBD, an automated framework that makes mixed-precision exploration computationally tractable while effectively identifying hardware-efficient configurations. Specifically, it performs physics-driven search space pruning via variable grouping and sensitivity analysis, and employs a data-efficient, prior-informed surrogate model to enable rapid trajectory error prediction. This formulation guides a hybrid optimizer to identify area- and power-efficient designs under user-defined accuracy and performance constraints. Experimental results demonstrate that APEX-RBD discovers designs achieving up to 1.9$\times$ area reduction and 1.8$\times$ power savings compared to uniform-precision baselines across diverse robotic platforms.

发表机构

• The Hong Kong University of Science and Technology（香港科技大学）
• The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））

机构由 AI 辅助整理，请以论文原文为准。