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Spiking neural network (SNN) has been attached to great importance due to the properties of high biological plausibility and low energy consumption on neuromorphic hardware.
Networks of spiking neurons: the third generation of neural network models
Wolfgang Maass · 1997
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip
Filipp Akopyan, Jun Sawada, Andrew Cassidy, Rodrigo Alvarez-Icaza, John Arthur, Paul Merolla, Nabil Imam, Yutaka Nakamura, Pallab Datta, Gi-Joon Nam, et al · 2015
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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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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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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
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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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Hybrid macro/micro level backpropagation for training deep spiking neural networks
Yingyezhe Jin, Wenrui Zhang, and Peng Li · 2018
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Deep neural networks with weighted spikes
Jaehyun Kim, Heesu Kim, Subin Huh, Jinho Lee, and Kiyoung Choi · 2018
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Deep learning with spiking neurons: opportunities and challenges
Michael Pfeiffer and Thomas Pfeil · 2018
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Slayer: Spike layer error reassignment in time
Sumit B Shrestha and Garrick Orchard · 2018
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Spatio-temporal backpropagation for training high-performance spiking neural networks
Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, and Luping Shi · 2018
Cited alongside, same era.
A plasticity-centric approach to train the non-differential spiking neural networks
Tielin Zhang, Yi Zeng, Dongcheng Zhao, and Mengting Shi · 2018
Cited alongside, same era.
Brain-inspired balanced tuning for spiking neural networks
Tielin Zhang, Yi Zeng, Dongcheng Zhao, and Bo Xu · 2018
Cited alongside, same era.
Fast and efficient information transmission with burst spikes in deep spiking neural networks
Seongsik Park, Seijoon Kim, Hyeokjun Choe, and Sungroh Yoon · 2019
Cited alongside, same era.
Towards artificial general intelligence with hybrid tianjic chip architecture
Jing Pei, Lei Deng, Sen Song, Mingguo Zhao, Youhui Zhang, Shuang Wu, Guanrui Wang, Zhe Zou, Zhenzhi Wu, Wei He, et al · 2019
Cited alongside, same era.
A fully spiking hybrid neural network for energy-efficient object detection
Biswadeep Chakraborty, Xueyuan She, and Saibal Mukhopadhyay · 2021
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Optimal conversion of conventional artificial neural networks to spiking neural networks
Shikuang Deng and Shi Gu · 2021
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Deep residual learning in spiking neural networks
Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, and Yonghong Tian · 2021
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Beyond classification: Directly training spiking neural networks for semantic segmentation
Youngeun Kim, Joshua Chough, and Priyadarshini Panda · 2021
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N-omniglot: a large-scale dataset for spatio-temporal sparse few-shot learning
Yang Li, Yiting Dong, Dongcheng Zhao, and Yi Zeng · 2021
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Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda · 2019
Cited alongside, same era.
Going deeper in spiking neural networks: Vgg and residual architectures
Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, and Kaushik Roy · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Deep spiking neural network: Energy efficiency through time based coding
Bing Han and Kaushik Roy · 2020
Cited alongside, same era.
Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network
Bing Han, Gopalakrishnan Srinivasan, and Kaushik Roy · 2020
Cited alongside, same era.
Towards fast and accurate object detection in bio-inspired spiking neural networks through bayesian optimization
Seijoon Kim, Seongsik Park, Byunggook Na, Jongwan Kim, and Sungroh Yoon · 2020
Cited alongside, same era.
Spiking-yolo: spiking neural network for energy-efficient object detection
Seijoon Kim, Seongsik Park, Byunggook Na, and Sungroh Yoon · 2020
Cited alongside, same era.
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Yang Li, Yi Zeng, and Dongcheng Zhao · 2021
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A free lunch from ann: Towards efficient, accurate spiking neural networks calibration
Yuhang Li, Shikuang Deng, Xin Dong, Ruihao Gong, and Shi Gu · 2021
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Retinanet object detector based on analog-to-spiking neural network conversion
Joaquín Royo Miquel, Silvia Tolu, Frederik ET Schöller, and Roberto Galeazzi · 2021
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A spiking neural network for image segmentation
Kinjal Patel, Eric Hunsberger, Sean Batir, and Chris Eliasmith · 2021
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Performance and energy efficiency analysis of machine learning algorithms towards green ai: a case study of decision tree algorithms
G Silva, B Schulze, and M Ferro · 2021
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Event-based backpropagation can compute exact gradients for spiking neural networks
Timo C Wunderlich and Christian Pehle · 2021
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Near lossless transfer learning for spiking neural networks
Zhanglu Yan, Jun Zhou, and Weng-Fai Wong · 2021
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Green artificial intelligence: Towards an efficient, sustainable and equitable technology for smart cities and futures
Tan Yigitcanlar, Rashid Mehmood, and Juan M Corchado · 2021
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Dongcheng Zhao, Yang Li, Yi Zeng, Jihang Wang, and Qian Zhang · 2021
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Dongcheng Zhao, Yi Zeng, and Yang Li · 2021
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Efficient and accurate conversion of spiking neural network with burst spikes
Yang Li and Yi Zeng · 2022
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