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This paper presents a new methodology to alleviate the fundamental trade-off between accuracy and latency in spiking neural networks (SNNs).
Networks of spiking neurons: the third generation of neural network models
Wolfgang Maass · 1997
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An introduction to roc analysis
Tom Fawcett · 2006
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The evolution of robotics research
Elena Garcia, Maria Antonia Jimenez, Pablo Gonzalez De Santos, and Manuel Armada · 2007
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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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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 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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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Dynamic capacity networks
Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, and Aaron Courville · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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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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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Efficient methods and hardware for deep learning
Song Han · 2017
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Multi-scale dense networks for resource efficient image classification
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Runtime neural pruning
Ji Lin, Yongming Rao, Jiwen Lu, and Jie Zhou · 2017
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Event-driven random back-propagation: Enabling neuromorphic deep learning machines
Emre O Neftci, Charles Augustine, Somnath Paul, and Georgios Detorakis · 2017
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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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Dynamic channel pruning: Feature boosting and suppression
Xitong Gao, Yiren Zhao, Łukasz Dudziak, Robert Mullins, and Cheng-zhong Xu · 2018
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Progressive neural architecture search
T2fsnn: Deep spiking neural networks with time-to-first-spike coding
Seongsik Park, Seijoon Kim, Byunggook Na, and Sungroh Yoon · 2020
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DRQ: Dynamic Region-based Quantization for Deep Neural Network Acceleration
Zhuoran Song, Bangqi Fu, Feiyang Wu, Zhaoming Jiang, Li Jiang, Naifeng Jing, and Xiaoyao Liang · 2020
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Optimal ann-snn conversion for high-accuracy and ultra-low-latency spiking neural networks
Tong Bu, Wei Fang, Jianhao Ding, PengLin Dai, Zhaofei Yu, and Tiejun Huang · 2021
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Comparison of artificial and spiking neural networks on digital hardware
Simon Davidson and Steve B Furber · 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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Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy · 2018
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Deep learning with spiking neurons: opportunities and challenges
Michael Pfeiffer and Thomas Pfeil · 2018
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Blockdrop: Dynamic inference paths in residual networks
Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S Davis, Kristen Grauman, and Rogerio Feris · 2018
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Learned step size quantization
Steven K Esser, Jeffrey L McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S Modha · 2019
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Channel gating neural networks
Weizhe Hua, Yuan Zhou, Christopher M De Sa, Zhiru Zhang, and G Edward Suh · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Jianhao Ding, Zhaofei Yu, Yonghong Tian, and Tiejun Huang · 2021
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Accelerating training of deep spiking neural networks with parameter initialization
Jianhao Ding, Jiyuan Zhang, Zhaofei Yu, and Tiejun Huang · 2021
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Towards biologically-plausible neuron models and firing rates in high-performance deep spiking neural networks
Chen Li and Steve Furber · 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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Are SNNs Really More Energy-Efficient Than ANNs? an In-Depth Hardware-Aware Study
Manon Dampfhoffer, Thomas Mesquida, Alexandre Valentian, and Lorena Anghel · 2022
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Snn-rat: Robustness-enhanced spiking neural network through regularized adversarial training
Jianhao Ding, Tong Bu, Zhaofei Yu, Tiejun Huang, and Jian Liu · 2022
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Structured Dynamic Precision for Deep Neural Networks Quantization
Kai Huang, Bowen Li, Dongliang Xiong, Haitian Jiang, Xiaowen Jiang, Xiaolang Yan, Luc Claesen, Dehong Liu, Junjian Chen, and Zhili Liu · 2022
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Neural architecture search for spiking neural networks
Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, and Priyadarshini Panda · 2022
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Quantization framework for fast spiking neural networks
Chen Li, Lei Ma, and Steve B Furber · 2022
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Converting artificial neural networks to spiking neural networks via parameter calibration
Yuhang Li, Shikuang Deng, Xin Dong, and Shi Gu · 2022
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On-device training under 256kb memory
Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, and Song Han · 2022
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Reducing ann-snn conversion error through residual membrane potential
Zecheng Hao, Tong Bu, Jianhao Ding, Tiejun Huang, and Zhaofei Yu · 2023
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Bridging the gap between anns and snns by calibrating offset spikes
Zecheng Hao, Jianhao Ding, Tong Bu, Tiejun Huang, and Zhaofei Yu · 2023
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