Oscillator-based Ising/Potts machines (OIMs/OPMs) are promising hardware accelerators for NP-hard combinatorial optimization problems using coupled oscillator synchronization dynamics. Analog OIMs/OPMs offer speed advantages but have limited coupling resolution, process variation susceptibility, and scalability issues, while digital GPU/CPU emulations provide flexibility but suffer from irregular memory access patterns and energy inefficiency. This work presents a custom ASIC architecture that digitally emulates OIM/OPM dynamics using simplified fixedpoint Kuramoto model equations. The scalable design features processing elements with direct interconnections, eliminating shared memory bottleneck while maintaining digital programmability and precision. A 20x20 processing element array with king's graph connectivity is prototyped and evaluated via post-layout simulations on unweighted/weighted max-cut and graph coloring problems, achieving 97-100% maximum accuracy with significant speed and energy improvements over general-purpose platforms, demonstrating the viability of algorithmically codesigned ASICs.

翻译：基于振荡器的Ising/Potts机（OIM/OPM）是利用耦合振荡器同步动力学解决NP-hard组合优化问题的有前途的硬件加速器。模拟OIM/OPM具有速度优势，但存在耦合分辨率有限、工艺偏差敏感以及可扩展性问题，而数字GPU/CPU模拟虽提供灵活性，却存在不规则内存访问模式和能效低下等缺陷。本文提出一种定制ASIC架构，通过简化的定点Kuramoto模型方程数字模拟OIM/OPM动力学。该可扩展设计采用具有直接互连的处理单元，在保持数字可编程性和精度的同时消除了共享内存瓶颈。通过后布局仿真原型化并评估了具有国王图连接性的20×20处理单元阵列，分别应用于无权重/有权重最大割问题和图着色问题，实现97-100%的最大准确率，并在速度和能效上较通用平台有显著提升，证明了算法协同设计的ASIC的可行性。