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Bio-inspired spiking neural networks (SNNs), operating with asynchronous binary signals (or spikes) distributed over time, can potentially lead to greater computational efficiency on event-driven hardware.
A tandem learning rule for efficient and rapid inference on deep spiking neural networks
Jibin Wu, Yansong Chua, Malu Zhang, Guoqi Li, Haizhou Li, and Kay Chen Tan · 1907
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Reliability of spike timing in neocortical neurons
Zachary F Mainen and Terrence J Sejnowski · 1995
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Spikeprop: backpropagation for networks of spiking neurons
Sander M Bohte, Joost N Kok, and Johannes A La Poutré · 2000
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Spiking neuron models: Single neurons, populations, plasticity
Wulfram Gerstner and Werner M Kistler · 2002
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Temporal spike sequence learning via backpropagation for deep spiking neural networks
Wenrui Zhang and Peng Li · 2008
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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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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Spinnaker: A 1-w 18-core system-on-chip for massively-parallel neural network simulation
Eustace Painkras, Luis A Plana, Jim Garside, Steve Temple, Francesco Galluppi, Cameron Patterson, David R Lester, Andrew D Brown, and Steve B Furber · 2013
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1.1 computing’s energy problem (and what we can do about it)
Mark Horowitz · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Spiking deep convolutional neural networks for energy-efficient object recognition
Yongqiang Cao, Yang Chen, and Deepak Khosla · 2015
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Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Peter U Diehl, Daniel Neil, Jonathan Binas, Matthew Cook, Shih-Chii Liu, and Michael Pfeiffer · 2015
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Song Han, Huizi Mao, and William J Dally · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Evaluating the energy efficiency of deep convolutional neural networks on cpus and gpus
Da Li, Xinbo Chen, Michela Becchi, and Ziliang Zong · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, and Colin J Akerman · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Google cloud doubles down on nvidia gpus for inference, 2019
Karl Freund · 2019
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Enabling spike-based backpropagation in state-of-the-art deep neural network architectures
Chankyu Lee, Syed Shakib Sarwar, and Kaushik Roy · 2019
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Surrogate gradient learning in spiking neural networks
Emre O Neftci, Hesham Mostafa, and Friedemann Zenke · 2019
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Going deeper in spiking neural networks: Vgg and residual architectures
Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, and Kaushik Roy · 2019
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Constructing energy-efficient mixed-precision neural networks through principal component analysis for edge intelligence
Indranil Chakraborty, Deboleena Roy, Isha Garg, Aayush Ankit, and Kaushik Roy · 2020
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Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, Michael Pfeiffer, and Shih-Chii Liu · 2017
Cited alongside, same era.
Deep learning with dynamic spiking neurons and fixed feedback weights
Arash Samadi, Timothy P Lillicrap, and Douglas B Tweed · 2017
Cited alongside, same era.
Escher: A cnn accelerator with flexible buffering to minimize off-chip transfer
Yongming Shen, Michael Ferdman, and Peter Milder · 2017
Cited alongside, same era.
Long short-term memory and learning-to-learn in networks of spiking neurons
Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert Legenstein, and Wolfgang Maass · 2018
Cited alongside, same era.
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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Amc: Automl for model compression and acceleration on mobile devices
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han · 2018
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Gradient descent for spiking neural networks
Dongsung Huh and Terrence J Sejnowski · 2018
Cited alongside, same era.
Temporal coding in spiking neural networks with alpha synaptic function
Iulia M Comsa, Thomas Fischbacher, Krzysztof Potempa, Andrea Gesmundo, Luca Versari, and Jyrki Alakuijala · 2020
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Leaky integrate-and-fire spiking neuron with learnable membrane time parameter
Wei Fang · 2020
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Neuromorphic nearest-neighbor search using intel’s pohoiki springs
E Paxon Frady, Garrick Orchard, David Florey, Nabil Imam, Ruokun Liu, Joyesh Mishra, Jonathan Tse, Andreas Wild, Friedrich T Sommer, and Mike Davies · 2020
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Rmp-snns: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural networks
Bing Han, Gopalakrishnan Srinivasan, and Kaushik Roy · 2020
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Temporal backpropagation for spiking neural networks with one spike per neuron
Saeed Reza Kheradpisheh and Timothee Masquelier · 2020
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Spike-flownet: Event-based optical flow estimation with energy-efficient hybrid neural networks
Chankyu Lee, Adarsh Kosta, Alex Zihao Zhu, Kenneth Chaney, Kostas Daniilidis, and Kaushik Roy · 2020
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Exploring the connection between binary and spiking neural networks
Sen Lu and Abhronil Sengupta · 2020
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Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation
Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, and Kaushik Roy · 2020
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Effective and efficient computation with multiple-timescale spiking recurrent neural networks
Bojian Yin, Federico Corradi, and Sander M Bohté · 2020
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