通过并行回火打破基于脉冲神经网络的约束满足问题求解器中的局部最小陷阱
Breaking Local-Minimum Traps in Spiking Neural Network-Based Solvers for CSPs via Parallel Tempering
AI总结：
研究针对基于脉冲神经网络的约束满足问题求解器易陷入局部最小陷阱的问题，提出将并行回火集成到神经采样求解器的方法，经实验验证该方法能提高硬实例成功概率，是并行回火在基于SNN的CSP求解器中的首次集成。
AI中文摘要：
具有随机神经元的脉冲神经网络（SNN）可通过连接性编码约束并通过脉冲动力学进行概率搜索来解决约束满足问题（CSP）。然而，固定温度随机动力学常陷入局部最小值（接近满足的配置），且随问题难度加剧。为克服此问题，我们将并行回火（PT）集成到神经采样求解器中，以不同逆温度运行多个并行副本。副本定期交换温度而非网络状态，在探索与围绕低能量配置的集中之间进行权衡，同时保留异步、基于脉冲的计算。我们使用来自SATLIB uf20 - 91基准的1000个实例，在相同计算资源下，将此架构与四个独立的固定温度求解器的并行基线进行评估。并行回火提高了332个实例的成功概率，仅使5个实例变差。关键在于，这些收益集中在独立求解器失败的硬实例上。违反轨迹分析证实了其潜在机制：温度交换使副本能够穿越固定温度动力学无法到达能量障碍，成功逃离限制基线的狭窄盆地。据我们所知，这是并行回火首次集成到基于SNN的CSP求解器中。
英文摘要：
Spiking neural networks (SNNs) with stochastic neurons can solve constraint satisfaction problems (CSPs) by encoding constraints via connectivity and performing probabilistic search via spike dynamics. However, fixed-temperature stochastic dynamics often get trapped in local minima - near-satisfying configurations - a vulnerability that escalates with problem difficulty. To overcome this, we integrate parallel tempering (PT) into the neural sampling solver, running multiple parallel replicas at varying inverse temperatures. Replicas periodically exchange temperatures rather than network states, managing the trade-off between exploration and concentration around low-energy configurations while preserving asynchronous, spike-based computation. We evaluate this architecture against a parallel baseline of four independent, fixed-temperature solvers using equal computational resources across 1000 instances from the SATLIB uf20-91 benchmark. Parallel tempering improves success probability on 332 instances while worsening only 5. Crucially, these gains are concentrated on hard instances where independent solvers fail. Violation trajectory analysis confirms the underlying mechanism: temperature exchanges allow replicas to traverse energy barriers unreachable by fixed-temperature dynamics, successfully escaping the narrow basins that constrain the baseline. To our knowledge, this represents the first integration of parallel tempering into an SNN-based CSP solver.
