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.

翻译：我们介绍NeuroSA，一种专门设计的神经形态架构，旨在利用基于Fowler-Nordheim量子力学隧穿的阈值退火过程，确保渐进收敛至伊辛问题的基态。NeuroSA的核心组件由一对异步开关神经元组成，它们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。每个开关神经元对的阈值由FN退火器自适应调整，由此产生的脉冲动力学复现了模拟退火的最优逃逸机制与收敛特性，尤其在低温条件下。为验证我们神经形态伊辛机的有效性，我们系统地求解了基准组合优化问题，如MAX-CUT和最大独立集。在多次运行中，NeuroSA持续生成的解分布始终集中在最先进结果附近（99%以内），或在最大独立集基准测试中超越了当前最先进的解。此外，NeuroSA无需任何针对特定图的超参数调整即可实现这些优越的分布。为提供实际例证，我们展示了在SpiNNaker2平台上实现NeuroSA的结果，突显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。