Quantization methods for SNNs produce different firing distributions at equivalent accuracy, and Earth Mover's Distance diagnoses this divergence better than accuracy alone.
Qp-snn: Quantized and pruned spiking neural networks.arXiv preprint arXiv:2502.05905,
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SAFA-SNN combines sparsity-aware spike dynamics and orthogonal subspace projection in spiking networks to achieve on-device few-shot class-incremental learning with lower energy use and reduced forgetting than prior baselines.
citing papers explorer
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Quantization of Spiking Neural Networks Beyond Accuracy
Quantization methods for SNNs produce different firing distributions at equivalent accuracy, and Earth Mover's Distance diagnoses this divergence better than accuracy alone.
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SAFA-SNN: Sparsity-Aware On-Device Few-Shot Class-Incremental Learning with Fast-Adaptive Structure of Spiking Neural Network
SAFA-SNN combines sparsity-aware spike dynamics and orthogonal subspace projection in spiking networks to achieve on-device few-shot class-incremental learning with lower energy use and reduced forgetting than prior baselines.