Learning from Dense Events: Towards Fast Spiking Neural Networks Training via Event Dataset Distillatio

• URL: http://arxiv.org/abs/2511.12095v1
• Date: Sat, 15 Nov 2025 08:20:47 GMT
• Title: Learning from Dense Events: Towards Fast Spiking Neural Networks Training via Event Dataset Distillatio
• Authors: Shuhan Ye, Yi Yu, Qixin Zhang, Chenqi Kong, Qiangqiang Wu, Kun Wang, Xudong Jiang,
• Abstract summary: Event cameras sense brightness changes and output binary asynchronous event streams, attracting increasing attention.<n>Their bio-inspired dynamics align well with neural networks (SNNs), offering a promising energy-efficient alternative to conventional vision systems.<n>We introduce bfPACE (text-Aligned Condensation for Events), the first dataset distillation framework to SNNs andtemporal event-based vision.
• Score: 33.31825132631613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event cameras sense brightness changes and output binary asynchronous event streams, attracting increasing attention. Their bio-inspired dynamics align well with spiking neural networks (SNNs), offering a promising energy-efficient alternative to conventional vision systems. However, SNNs remain costly to train due to temporal coding, which limits their practical deployment. To alleviate the high training cost of SNNs, we introduce \textbf{PACE} (Phase-Aligned Condensation for Events), the first dataset distillation framework to SNNs and event-based vision. PACE distills a large training dataset into a compact synthetic one that enables fast SNN training, which is achieved by two core modules: \textbf{ST-DSM} and \textbf{PEQ-N}. ST-DSM uses residual membrane potentials to densify spike-based features (SDR) and to perform fine-grained spatiotemporal matching of amplitude and phase (ST-SM), while PEQ-N provides a plug-and-play straight through probabilistic integer quantizer compatible with standard event-frame pipelines. Across DVS-Gesture, CIFAR10-DVS, and N-MNIST datasets, PACE outperforms existing coreset selection and dataset distillation baselines, with particularly strong gains on dynamic event streams and at low or moderate IPC. Specifically, on N-MNIST, it achieves \(84.4\%\) accuracy, about \(85\%\) of the full training set performance, while reducing training time by more than \(50\times\) and storage cost by \(6000\times\), yielding compact surrogates that enable minute-scale SNN training and efficient edge deployment.

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