AI 中文总结
本文提出基于块吉布斯采样的多芯片PASS集成方法，扩展概率伊辛机规模，在最大割问题上实现千倍加速，并展示常数级扩展定律优势。
AI 中文摘要
伊辛机在高效解决NP难优化问题方面具有巨大潜力，这类问题使用传统计算架构难以处理。许多此类优化问题属于统计可学习范畴，涉及在非凸能量景观中搜索，从众多可能的、近似相似的配置中找到最优解。在此背景下，概率玻尔兹曼机架构，特别是PASS（并行异步随机采样器），探索并建模了与所有可能配置相关的复杂概率景观，并擅长找到这些棘手问题的基态能量解。此外，基于噪声的神经元架构解决了传统退火方法可能陷入局部最小值的局限性。在此，我们展示了一种基于块吉布斯采样的随机采样方法，用于集成多个异步PASS芯片（本工作中为四个），从而实现更好的可扩展性。进一步，我们通过映射784节点最大割问题来展示扩展能力，该问题集成了采用14纳米CMOS FinFET技术制造的256节点PASS加速器。采用块吉布斯采样协议的PASS系统在最大割优化方面，相比在CPU和GPU上实现的最先进方法，实现了约1000倍的加速。该方法的普遍适用性还通过求解量子自旋链横向伊辛系统并准确表示复杂概率景观得到进一步证明。此外，我们的结果表明，在扩展的PASS加速器中，扩展定律变为常数，而在GPU上则为指数级，从而在求解时间上实现了至少4个数量级的改进。因此，所提出的方法为扩展不遵循任何时钟进行操作的异步类脑动力学系统提供了途径。
英文摘要
Ising Machines offer vast potential to solve NP-hard optimization problems efficiently that are intractable to solve using conventional computing architecture. A lot of these optimization problems fall into statistical learnability and involve finding an optimal solution among many possible, near-analogous configurations, by searching in a non-convex energy landscape. In this context, the probabilistic Boltzmann machine architecture especially PASS (Parallel Asynchronous Stochastic Sampler), explores and models the complex probability landscape pertaining to all possible configurations and excels in finding the ground-state energy solution of these intractable problems. Additionally, the noise-based neuron architecture addresses the limitation of conventional annealing methods, which may get stuck around local minima. Here, we demonstrate a stochastic sampling approach based on Block Gibbs Sampling to integrate multiple asynchronous PASS chips (four in this work), enabling improved scalability. Further, we demonstrate the scaling by mapping 784 nodes Max-Cut problem integrating 256 nodes PASS accelerator manufactured in 14 nm CMOS FinFET technology. PASS-enabled system with Block Gibbs Sampling protocol shows approximately 1000 times speedup for Max-Cut optimization compared to state-of-the-art methods implemented on CPUs and GPUs. The general applicability of this approach is further illustrated by solving a quantum spin chain Transverse Ising system and accurately representing complex probability landscapes. Moreover, our results demonstrate the change in the scaling law to constant in the scaled-PASS accelerator as compared to exponential on GPUs enabling at least 4 orders of magnitude improvement in time-to-solution. Hence, the presented methodology enables the pathway for scaling of asynchronous brain-like dynamics systems that do not follow any clock for its operation.
Comments19 pages, 6 figures
