SkewSpike: A Dictionary-based Skewed-Tree Framework for Co-Firing Spike Compression in Spiking Neural Networks

Spiking Neural Networks (SNNs) offer a promising energy-efficient alternative to conventional neural networks due to their event-driven execution. However, modern deep SNNs predominantly utilize rate coding, generating excessive spike volumes that saturate Address Event Representation (AER) communication channels on neuromorphic hardware. In this work, we introduce SkewSpike, a hierarchical dictionary-based AER spike compression framework designed to alleviate this communication bottleneck by exploiting skewed neuronal co-firing activity. SkewSpike maps recurring synchronous spike patterns into compact symbolic AER packets via a right-skewed tree dictionary, while further sparsifying residual communication traffic through stochastic spike pruning. To prevent significant accuracy degradation caused by residual spike pruning, the framework incorporates an adaptive threshold calibration mechanism that preserves rare features within the activity distribution, alongside a deterministic, model-agnostic autotuner that discovers optimal configurations for target AER compression ratios. We evaluate SkewSpike across diverse spiking architectures and datasets, including event-based neuromorphic benchmarks and full-scale ImageNet-1k. Experimental results demonstrate that SkewSpike substantially outperforms standard Huffman coding and existing AER compression baselines, achieving up to 75.41% AER event volume reduction on fully connected models with negligible accuracy loss, and maintaining 50.38% AER compression on ImageNet-1k with only a 2.05% accuracy drop.