基于伊辛模型自动构建可自适应的随机处理器，用于求解组合优化问题。
A Tool for the Synthesis of Adaptive Probabilistic Processors Based on the Ising Model
- 根据问题规模与拓扑自动构造伊辛哈密顿量并确定p-bit数量
- 在基准测试中收敛性优于固定策略，提升求解效率
- 支持MTJ和p-bit硬件实现，适合未来随机计算系统研发
本文提出一种工具，用于合成与仿真基于伊辛模型的随机架构，以解决组合优化问题。该方法可根据问题规模与拓扑自动构建伊辛哈密顿量，并确定所需的概率元件（p-bits）数量。同时，工具引入自适应策略，在吉布斯采样、模拟退火（SA）、模拟量子退火（SQA）及基于簇的方法中动态选择最优更新算法。实验结果表明，相比固定方法，该框架在收敛行为与灵活性方面均有提升。该框架支持对随机计算策略的系统评估，并为基于磁性隧道结（MTJs）和p-bits的未来硬件实现提供支撑。
原文摘要 · Abstract (English)
This work presents a tool for the synthesis and simulation of probabilistic architectures for solving combinatorial optimization problems by mapping them to the Ising model. The proposed approach automatically constructs the Ising Hamiltonian and determines the number of probabilistic elements (p-bits) based on problem characteristics such as size and topology. Furthermore, the tool introduces an adaptive strategy for selecting the most suitable update algorithm among Gibbs Sampling, Simulated Annealing (SA), Simulated Quantum Annealing (SQA), and cluster-based methods. Experimental results using benchmark problems demonstrate improved convergence behavior and flexibility compared to fixed approaches. The proposed framework enables systematic evaluation of probabilistic computing strategies and supports the development of future hardware implementations based on MTJs and p-bits.
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