用脉冲神经网络实现低延迟事件驱动的网络状态预测。
Network-Optimised Spiking Neural Network for Event-Driven Networking
- 设计可训练的脉冲单元，按事件触发更新，节省计算资源。
- 在队列遥测任务中，检测准确率和响应延迟优于传统模型。
- 适合边缘设备部署，提供资源受限场景下的配置指导。
延迟耦合系统常需从稀疏遥测数据中快速决策，而密集固定步长的神经推断效率低下，可能削弱近稳定边界性能。本文提出可训练的两态事件驱动动力学单元 Network-Optimised Spiking (NOS)，适用于有延迟的图结构流数据。其状态映射至快速负载变量与慢速恢复资源。NOS采用有限缓冲区的有界兴奋性、显式泄漏项以实现服务与阻尼，并通过每边门控与通信延迟实现图局部耦合，支持代理梯度训练与类脑执行。我们证明了亚阈值平衡点的存在性与唯一性，推导出基于雅可比矩阵的稳定性条件，获得仅依赖图谱的标量稳定性阈值。随机到达模型揭示系统趋近稳定边界时波动性上升。在延迟图预测与早期预警任务中，基于队列遥测数据，NOS 在相同残差协议下显著优于 MLP、RNN/GRU 和时序 GNN 基线，同时提供资源受限部署的校准规则。
原文摘要 · Abstract (English)
Delay-coupled systems often require low-latency decisions from sparse telemetry, where dense fixed-step neural inference is wasteful and can degrade near stability margins. We introduce Network-Optimised Spiking (NOS), a trainable two-state event-driven dynamical unit for delayed, graph-coupled streams, whose states map to a fast load variable and a slower recovery resource. NOS uses bounded excitability for finite buffers, explicit leak terms for service and damping, and graph-local coupling with per-link gates and communication delays, with differentiable resets compatible with surrogate-gradient training and neuromorphic execution. We prove existence and uniqueness of subthreshold equilibria, derive Jacobian-based stability conditions, and obtain a scalar network stability threshold that separates topology from node dynamics via a Perron-mode spectral condition. A stochastic arrival model aligned with telemetry smoothing explains increased variability as systems approach stability boundaries. On delayed graph forecasting and early-warning tasks from queue telemetry, NOS improves detection F1 and detection latency over MLP, RNN/GRU, and temporal GNN baselines under a common residual-based protocol, while providing calibration rules for resource-constrained deployments. Code and Demos: https://mbilal84.github.io/nos-snn-networking/
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