用芯片级自旋模型加速多机器人目标规划，能效提升千倍以上。
Ising Acceleration for Multi-Robot Multi-Target Planning
- 设计新算法与映射管道，适配有限自旋数和系数精度的硬件限制。
- 在真实45自旋芯片上实现8000倍能效提升，端到端路线仅差经典方法9%。
- 适合低功耗机器人系统中需快速求解组合优化的场景。
Ising机器正成为组合优化的有前景硬件。随着CMOS Ising技术进步，其在能源受限的机器人领域日益吸引人，因机器人任务常涉及多种组合优化问题。然而，尚缺乏对这类芯片在机器人规划架构中定位的硬件感知分析。本文研究了CMOS Ising机器在多机器人多目标规划中的能力与局限性。通过使用真实的45自旋全连接CMOS Ising芯片作为代表设备，分析了目标共享、路径构建和路径查找三个规划层级。提出基于Ising的新规划方法及多映射流水线，包含自旋合并、系数量化与自旋预算分支，以适应硬件的自旋数与系数精度限制。结果表明，所提递归目标共享方法天然匹配硬件，在45自旋芯片上实现高达8000倍的能效优势。端到端流程生成的路径与强基线相比仅差9%，但能耗降低130倍，证明紧凑型CMOS Ising机器可在规划栈特定环节有效应用。
原文摘要 · Abstract (English)
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.
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