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.
ProofPaper ↗
Key points
- 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 therefore propose SpikeQ, a spike-guided PTQ framework.
- We evaluate SpikeQ on seven benchmarks of image classification, semantic segmentation, and object detection.
Sources (1)
- [1]SpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural NetworksarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 07:24 AM
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).
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