SpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural Networks
On neuromorphic chips, spiking neural networks (SNNs) consume far less energy than artificial neural networks (ANNs), but the limited memory of these chips requires low-bit synaptic weights and membrane potentials. Existing post-training quantization (PTQ) methods provide them without retraining and, like those for ANNs, minimize numerical errors such as the mean squared error (MSE). In an SNN, however, a quantization error reaches later layers only through the spikes it flips. We find that errors of equal MSE can cause very different accuracy losses, whereas errors that flip the same fraction of spikes cause the same loss. We therefore propose SpikeQ, a spike-guided PTQ framework. For we...
| Authors | Kai Sun, Peibo Duan, Zhanglu Yan et al. |
|---|---|
| Primary category | cs.NE |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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