Quantisation and pooling method for low‐inference‐latency spiking neural networks-Reference-Cited by-同舟云学术

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Quantisation and pooling method for low‐inference‐latency spiking neural networks

Published:2017-09 Issue:20 Volume:53 Page:1347-1348
ISSN:0013-5194
Container-title:Electronics Letters
language:en
Short-container-title:Electron. lett.

Author:

Lin Zhitao¹,Shen Juncheng¹,Ma De²,Meng Jianyi³

Affiliation:

1. Institute of VLSI Design, Zhejiang UniversityHangzhouPeople's Republic of China

2. College of Computer ScienceZhejiang UniversityHangzhouPeople's Republic of China

3. State Key Laboratory of ASIC and SystemFudan UniversityShanghaiPeople's Republic of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/el.2017.2219

Reference6 articles.

1. Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition

2. Neuromorphic VLSI Bayesian integration synapse

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1. Neuron pruning in temporal domain for energy efficient SNN processor design;Frontiers in Neuroscience;2023-11-30

2. Spiking CMOS-NVM mixed-signal neuromorphic ConvNet with circuit- and training-optimized temporal subsampling;Frontiers in Neuroscience;2023-07-18

3. Spiking Approximations of the MaxPooling Operation in Deep SNNs;2022 International Joint Conference on Neural Networks (IJCNN);2022-07-18

4. A Rapid Spiking Neural Network Approach With an Application on Hand Gesture Recognition;IEEE Transactions on Cognitive and Developmental Systems;2021-03

5. Improved integrate-and-fire neuron models for inference acceleration of spiking neural networks;Applied Intelligence;2020-11-04

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