CQP fuses magnitude and criticality into an importance metric for iterative SNN pruning, delivering 95.6% MNIST accuracy at 90% sparsity and 73% energy reduction at 70% sparsity.
Shi et al., Soft pruning for spiking neural networks, IEEE Trans
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Criticality-Constrained Iterative Pruning for Energy-Efficient Spiking Neural Networks via Combined Importance Scoring
CQP fuses magnitude and criticality into an importance metric for iterative SNN pruning, delivering 95.6% MNIST accuracy at 90% sparsity and 73% energy reduction at 70% sparsity.