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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.NE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.