SpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural Networks
In brief
- SpikeQ optimizes SNN quantization by targeting spike errors instead of numerical errors.
- With 4-bit weights and membrane potentials, accuracy remains close to full precision.
- The method cuts memory usage by up to 7.9x and energy consumption by up to 6.9x.
SpikeQ introduces a post-training quantization method tailored for spiking neural networks (SNNs) on neuromorphic chips. It minimizes quantization errors by focusing on spike flips rather than traditional numerical errors. Results show minimal accuracy loss with 4-bit weights and membrane potentials, while reducing memory footprint and energy consumption.
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