AI 中文总结
本文提出一种基于朗之万动力学的伊辛机硬件退火算法，经仿真验证其能以86.5%概率引导系统达基态，解质量提升97.5%且求解时间缩短50%。
AI 中文摘要
伊辛机是一种非冯·诺依曼架构的机器，旨在通过在伊辛模型中搜索基态（即最低能量构型）来解决组合优化问题（COP）。然而，伊辛机常因复杂的能量景观而陷入局部极小值的挑战。硬件退火算法通过概率方法引导系统趋向基态，以缓解该问题。本文提出一种基于朗之万动力学（一种由随机噪声产生的随机扰动）的伊辛机硬件退火算法，完成了理论分析、系统级设计及详细电路设计。采用标准65nm CMOS工艺通过芯片级仿真评估算法性能以验证其有效性，结果表明，所提硬件退火算法能以86.5%的概率有效引导系统到达基态，使解的质量提升97.5%；此外，通过行为级仿真将该算法与最先进的硬件退火方法对比，凸显其在解质量提升的同时，将求解时间缩短50%。
英文摘要
Ising machines are non-von Neumann machines designed to solve combinatorial optimization problems (COP) by searching for the ground state, or the lowest energy configuration, within the Ising model. However, Ising machines often face the challenges of getting trapped in local minima due to the complex energy landscapes. Hardware annealing algorithms help mitigate this issue by using a probabilistic approach to steer the system toward the ground state. In this paper, we present a hardware annealing algorithm for Ising machines based on Langevin dynamics, a stochastic perturbation by random noise. Theoretical analysis, system-level design, and detailed circuit design are carried out. We evaluate the performance of the algorithm through chip-level simulation using a standard 65-nm CMOS technology to demonstrate the algorithm's efficacy. The results show that the proposed hardware annealing algorithm effectively guides the system to reach the ground state with a probability of 86.5%, significantly improving the solution quality by 97.5%. Further, we compare the algorithm with state-of-the-art hardware annealing methods through behavioral-level simulations, highlighting its improved solution quality alongside a 50% reduction in time-to-solution.
