MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks
Author:
Affiliation:
1. Yale University,Department of Electrical Engineering,USA
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10473777/10473787/10473825.pdf?arnumber=10473825
Reference27 articles.
1. Towards spike-based machine intelligence with neuromorphic computing
2. Direct Training for Spiking Neural Networks: Faster, Larger, Better
3. Temporal spike sequence learning via backpropagation for deep spiking neural networks;Zhang
4. Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing
5. ImageNet: A large-scale hierarchical image database
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1. SpQuant-SNN: ultra-low precision membrane potential with sparse activations unlock the potential of on-device spiking neural networks applications;Frontiers in Neuroscience;2024-09-04
2. Toward Efficient Deep Spiking Neuron Networks: A Survey on Compression;Communications in Computer and Information Science;2024
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