APEX-RBD: Mixed-Precision Exploration Framework for Hardware-Efficient Robot Dynamics Accelerator Design

• URL: http://arxiv.org/abs/2609.05161v1
• Date: Fri, 04 Sep 2026 14:02:44 GMT
• Title: APEX-RBD: Mixed-Precision Exploration Framework for Hardware-Efficient Robot Dynamics Accelerator Design
• Abstract summary: APEX-RBD is an automated framework that makes mixed-precision exploration computationally tractable and identifies hardware-efficient configurations.<n>Results demonstrate that APEX-RBD discovers designs achieving up to 1.9$times$ area reduction and 1.8$times$ power savings.
• Score: 9.797891283123723
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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