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Spiking Neural Networks (SNNs) have been widely praised for their high energy efficiency and immense potential.
Networks of spiking neurons: the third generation of neural network models
Wolfgang Maass · 1997
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Error-backpropagation in networks of fractionally predictive spiking neurons
Sander M Bohte · 2011
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A million spiking-neuron integrated circuit with a scalable communication network and interface
Paul A Merolla, John V Arthur, Rodrigo Alvarez-Icaza, Andrew S Cassidy, Jun Sawada, Filipp Akopyan, Bryan L Jackson, Nabil Imam, Chen Guo, Yutaka Nakamura, et al · 2014
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Converting static image datasets to spiking neuromorphic datasets using saccades
Garrick Orchard, Ajinkya Jayawant, Gregory K Cohen, and Nitish Thakor · 2015
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Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
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A low power, fully event-based gesture recognition system
Arnon Amir, Brian Taba, David Berg, Timothy Melano, Jeffrey McKinstry, Carmelo Di Nolfo, Tapan Nayak, Alexander Andreopoulos, Guillaume Garreau, Marcela Mendoza, et al · 2017
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Cifar10-dvs: an event-stream dataset for object classification
Hongmin Li, Hanchao Liu, Xiangyang Ji, Guoqi Li, and Luping Shi · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Loihi: A neuromorphic manycore processor with on-chip learning
Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, et al · 2018
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Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2018
Cited alongside, same era.
A highly effective and robust membrane potential-driven supervised learning method for spiking neurons
Malu Zhang, Hong Qu, Ammar Belatreche, Yi Chen, and Zhang Yi · 2018
Cited alongside, same era.
Compressing bert: Studying the effects of weight pruning on transfer learning
Mitchell A Gordon, Kevin Duh, and Nicholas Andrews · 2020
Cited alongside, same era.
Up or down? adaptive rounding for post-training quantization
Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, and Tijmen Blankevoort · 2020
Cited alongside, same era.
Going deeper with directly-trained larger spiking neural networks
Hanle Zheng, Yujie Wu, Lei Deng, Yifan Hu, and Guoqi Li · 2021
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Temporal efficient training of spiking neural network via gradient re-weighting
Shikuang Deng, Yuhang Li, Shanghang Zhang, and Shi Gu · 2022
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Q-vit: Accurate and fully quantized low-bit vision transformer
Yanjing Li, Sheng Xu, Baochang Zhang, Xianbin Cao, Peng Gao, and Guodong Guo · 2022
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Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks
Guobin Shen, Dongcheng Zhao, and Yi Zeng · 2022
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Pokebnn: A binary pursuit of lightweight accuracy
Yichi Zhang, Zhiru Zhang, and Lukasz Lew · 2022
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Nitin Rathi and Kaushik Roy · 2020
Cited alongside, same era.
Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, and Kaushik Roy · 2020
Cited alongside, same era.
Movement pruning: Adaptive sparsity by fine-tuning
Victor Sanh, Thomas Wolf, and Alexander Rush · 2020
Cited alongside, same era.
Spiking deep residual networks
Yangfan Hu, Huajin Tang, and Gang Pan · 2021
Cited alongside, same era.
Fq-vit: Post-training quantization for fully quantized vision transformer
Yang Lin, Tianyu Zhang, Peiqin Sun, Zheng Li, and Shuchang Zhou · 2021
Cited alongside, same era.
Post-training quantization for vision transformer
Zhenhua Liu, Yunhe Wang, Kai Han, Wei Zhang, Siwei Ma, and Wen Gao · 2021
Cited alongside, same era.
Progressive tandem learning for pattern recognition with deep spiking neural networks
Jibin Wu, Chenglin Xu, Xiao Han, Daquan Zhou, Malu Zhang, Haizhou Li, and Kay Chen Tan · 2021
Cited alongside, same era.
Rectified linear postsynaptic potential function for backpropagation in deep spiking neural networks
Malu Zhang, Jiadong Wang, Jibin Wu, Ammar Belatreche, Burin Amornpaisannon, Zhixuan Zhang, Venkata Pavan Kumar Miriyala, Hong Qu, Yansong Chua, Trevor E Carlson, et al · 2021
Cited alongside, same era.
Zhaokun Zhou, Yuesheng Zhu, Chao He, Yaowei Wang, Shuicheng Yan, Yonghong Tian, and Li Yuan · 2022
Later among the works it cites.
Enhancing efficient continual learning with dynamic structure development of spiking neural networks
Bing Han, Feifei Zhao, Yi Zeng, Wenxuan Pan, and Guobin Shen · 2023
Closest in time.
Firefly: A high-throughput hardware accelerator for spiking neural networks with efficient dsp and memory optimization
Jindong Li, Guobin Shen, Dongcheng Zhao, Qian Zhang, and Yi Zeng · 2023
Closest in time.
Man Yao, Jiakui Hu, Zhaokun Zhou, Li Yuan, Yonghong Tian, Bo Xu, and Guoqi Li · 2023
Closest in time.
Braincog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired ai and brain simulation
Yi Zeng, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yiting Dong, Enmeng Lu, Qian Zhang, Yinqian Sun, Qian Liang, Yuxuan Zhao, et al · 2023
Closest in time.
Dongcheng Zhao, Guobin Shen, Yiting Dong, Yang Li, and Yi Zeng · 2023
Closest in time.
Spikegpt: Generative pre-trained language model with spiking neural networks
Rui-Jie Zhu, Qihang Zhao, and Jason K Eshraghian · 2023
Closest in time.