发表机构
University of Minnesota（明尼苏达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究CMOS伊辛机在多机器人多目标规划中的加速能力，提出适配硬件的规划方法与流水线，其能耗远低于经典方案，路径质量接近经典基准，可用于规划栈的部分环节。
AI 中文摘要
伊辛机正成为组合优化领域极具潜力的硬件。随着CMOS伊辛技术的最新进展，它们作为低功耗加速器系统在机器人领域愈发受到关注，机器人领域存在能量受限及多种形式的组合优化问题。然而，目前仍缺乏针对这类芯片在机器人规划栈中适配场景的硬件感知分析。本文研究CMOS伊辛机在多机器人多目标规划中低功耗加速的能力与局限性，以真实的45自旋全连接CMOS伊辛芯片为代表设备，分析目标共享、路径构建、路径规划三个规划层。我们提出新的基于伊辛的规划方法及多映射流水线，该流水线采用自旋合并、系数量化、自旋预算分支策略，使子问题适配自旋和系数受限的硬件。结果表明，所提递归目标共享方法天然适配伊辛硬件，能耗较经典基准低达8000倍；端到端来看，伊辛流水线生成的路径与强经典基准的差距在9%以内，能耗低130倍，说明紧凑型CMOS伊辛机可在规划栈的选定部分发挥作用。
英文摘要
Ising machines are emerging as promising hardware for combinatorial optimization. With recent advances in CMOS Ising technology, they are becoming attractive as low-power accelerator systems for robotics, where energy is limited and combinatorial optimization arises in multiple forms. However, a hardware-aware analysis of where such chips fit within a robotics planning stack is still missing. This paper studies the capabilities and limitations of CMOS Ising machines for low-power acceleration in multi-robot multi-target planning. We analyze three planning layers---target sharing, tour construction, and pathfinding---using real 45-spin all-to-all connected CMOS Ising chips as representative devices. We propose new Ising-based planning methods and a multi-mapping pipeline that uses spin merging, coefficient quantization, and spin-budget branching to adapt subproblems to spin- and coefficient-limited hardware. Our results show that the proposed recursive target-sharing method naturally matches the Ising hardware, achieving up to 8,000x lower energy than a classical baseline. End to end, the Ising pipeline produces routes within 9% of a strong classical baseline at 130x lower energy, showing that compact CMOS Ising machines can be effective in selected parts of the planning stack.
Comments11 pages, 14 figures
