SparseForge: Beyond Spike Sparsity toward Hardware-Exploitable Neuron Pruning for Efficient SNN Acceleration
- DOI
- 10.48550/arxiv.2611.01611
- Published
- 2026
- Container
- Not recorded
- Publisher
- arXiv
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.48550/arxiv.2611.01611,
title = {SparseForge: Beyond Spike Sparsity toward Hardware-Exploitable Neuron Pruning for Efficient SNN Acceleration},
author = {Wang, Kainan and Fu, Sanhe and Wang, Aili},
year = {2026},
doi = {10.48550/arxiv.2611.01611},
url = {https://doi.org/10.48550/arxiv.2611.01611}
}RIS
TY - JOUR TI - SparseForge: Beyond Spike Sparsity toward Hardware-Exploitable Neuron Pruning for Efficient SNN Acceleration AU - Wang, Kainan AU - Fu, Sanhe AU - Wang, Aili PY - 2026 DO - 10.48550/arxiv.2611.01611 UR - https://doi.org/10.48550/arxiv.2611.01611 ER -
APA
Kainan, W., Sanhe, F., & Aili, W. (2026). SparseForge: Beyond Spike Sparsity toward Hardware-Exploitable Neuron Pruning for Efficient SNN Acceleration. https://doi.org/10.48550/arxiv.2611.01611
Source records
- datacite · retrieved 2026-11-07T03:03:12.621Z