Snowball通过双模式采样与异步更新，大幅加速组合优化求解。
Snowball: A Scalable All-to-All Ising Machine with Dual-Mode Markov Chain Monte Carlo Spin Selection and Asynchronous Spin Updates for Fast Combinatorial Optimization
- 双模式MCMC选自旋，异步更新避免震荡
- 在相同测试集上比现有机器快8倍
- 数字架构支持高精度耦合系数配置
Ising机器作为组合优化加速器已崭露头角。为实现实际部署，本文针对三大挑战展开：（1）硬件拓扑限制，（2）自旋选择与更新算法效率，（3）可扩展的耦合系数精度。受限拓扑需嵌入映射；朴素并行更新易导致振荡或停滞；精度不足则可能无法实现可行映射或降低解质量。本文提出Snowball，一种数字可扩展、全连接的Ising机器，集成双模式马尔可夫链蒙特卡洛自旋选择与异步自旋更新机制，提升收敛速度并缩短求解时间。其数字架构支持宽范围可配置的耦合精度，优于多数高比特位数的模拟实现。在AMD Alveo U250加速卡上的原型系统，在相同基准实例上相较当前最优Ising机器实现8倍时间节省。
原文摘要 · Abstract (English)
Ising machines have emerged as accelerators for combinatorial optimization. To enable practical deployment, this work aims to reduce time-to-solution by addressing three challenges: (1) hardware topology, (2) spin selection and update algorithms, and (3) scalable coupling-coefficient precision. Restricted topologies require minor embedding; naive parallel updates can oscillate or stall; and limited precision can preclude feasible mappings or degrade solution quality. This work presents Snowball, a digital, scalable, all-to-all coupled Ising machine that integrates dual-mode Markov chain Monte Carlo spin selection with asynchronous spin updates to promote convergence and reduce time-to-solution. The digital architecture supports wide, configurable coupling precision, unlike many analog realizations at high bit widths. A prototype on an AMD Alveo U250 accelerator card achieves an 8$\times$ reduction in time-to-solution relative to a state-of-the-art Ising machine on the same benchmark instance.
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