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APEX-RBD: Mixed-Precision Exploration Framework for Hardware-Efficient Robot Dynamics Accelerator Design — arXiv:2609.05161

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.05161
License (as archived): CC0-1.0
Archived: 2026-10-16T22:42:19.989Z
SHA-256 of the archived text: 4206b5fd08d6ff31b3ac3c102694ff85bc6d05bb58abde2253bc49a77bef6bf6
Stable URL of this page: https://metraipolis.com/documento/dc_c8e39ffec560

How to cite

APEX-RBD: Mixed-Precision Exploration Framework for Hardware-Efficient Robot Dynamics Accelerator Design — arXiv:2609.05161 — arXiv (en), https://arxiv.org/abs/2609.05161 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c8e39ffec560 (Metraipolis archive dc_c8e39ffec560, sha256 4206b5fd08d6ff31b3ac3c102694ff85bc6d05bb58abde2253bc49a77bef6bf6)

BibTeX

@misc{dcc8e39ffec560,
  title = {APEX-RBD: Mixed-Precision Exploration Framework for Hardware-Efficient Robot Dynamics Accelerator Design — arXiv:2609.05161},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.05161},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c8e39ffec560; sha256 4206b5fd08d6ff31b3ac3c102694ff85bc6d05bb58abde2253bc49a77bef6bf6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.05161. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-07 in cs.AR).
Categorie arXiv: Hardware Architecture (cs.AR) ; Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.05161 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T22:26:48.914Z dalla pagina https://arxiv.org/catchup/cs.AR/2026-09-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).