Chen Zihao, Xiao Zhili, Akl Mahmoud, Leugring Johannes, Olajide Omowuyi, Malik Adil, Dennler Nik, Harper Chad, Bose Subhankar, Gonzalez Hector A, Samaali Mohamed, Liu Gengting, Eshraghian Jason, Pignari Riccardo, Urgese Gianvito, Andreou Andreas G, Shankar Sadasivan, Mayr Christian, Cauwenberghs Gert, Chakrabartty Shantanu
Department of Electrical and Systems Engineering, Washington University in St. Louis, One Brookings Drive, St. Louis, MO, 63130, USA.
SpiNNcloud Systems GmbH, Freibergerstr. 37, Dresden, 01067, Germany.
Nat Commun. 2025 Mar 31;16(1):3086. doi: 10.1038/s41467-025-58231-5.
We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using a Fowler-Nordheim quantum mechanical tunneling based threshold-annealing process. The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing dynamics onto a network of integrate-and-fire neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer and the resulting spiking dynamics replicates the optimal escape mechanism and convergence of SA, particularly at low-temperatures. To validate the effectiveness of our neuromorphic Ising machine, we systematically solved benchmark combinatorial optimization problems such as MAX-CUT and Max Independent Set. Across multiple runs, NeuroSA consistently generates distribution of solutions that are concentrated around the state-of-the-art results (within 99%) or surpass the current state-of-the-art solutions for Max Independent Set benchmarks. Furthermore, NeuroSA is able to achieve these superior distributions without any graph-specific hyperparameter tuning. For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform, highlighting the feasibility of mapping our proposed architecture onto a standard neuromorphic accelerator platform.
我们介绍了NeuroSA，这是一种神经形态架构，专门设计用于通过基于福勒-诺德海姆量子力学隧穿的阈值退火过程，确保渐近收敛到伊辛问题的基态。NeuroSA的核心组件由一对异步通断神经元组成，它有效地将经典模拟退火动力学映射到积分发放神经元网络上。每个通断神经元对的阈值由一个FN退火器自适应调整，由此产生的脉冲发放动力学复制了模拟退火的最优逃逸机制和收敛性，特别是在低温情况下。为了验证我们的神经形态伊辛机的有效性，我们系统地解决了诸如最大割和最大独立集等基准组合优化问题。在多次运行中，NeuroSA始终生成集中在当前最优结果（99%以内）附近的解分布，或者在最大独立集基准测试中超过当前最优解。此外，NeuroSA能够在不进行任何特定于图的超参数调整的情况下实现这些优异的分布。为了进行实际说明，我们展示了在SpiNNaker2平台上实现NeuroSA的结果，突出了将我们提出的架构映射到标准神经形态加速器平台上的可行性。
