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Event-based neuromorphic systems promise to reduce the energy consumption of deep learning tasks by replacing expensive floating point operations on dense matrices by low power sparse and asynchronous operations on spike events.
Networks of Spiking Neurons: The Third Generation of Neural Network Models
W. Maass · 1997
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A million spiking-neuron integrated circuit with a scalable communication network and interface
P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, Y. Nakamura, B. Brezzo, I. Vo, S. K. Esser, R. Appuswamy, B. Taba, A. Amir, M. D. Flickner, W. P. Risk, R. Manohar, and D. S. Modha · 2014
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Fast-Classifying, High-Accuracy Spiking Deep Networks Through Weight and Threshold Balancing
P. U. Diehl, D. Neil, J. Binas, M. Cook, S.-C. Liu, and M. Pfeiffer · 2015
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A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses
N. Qiao, H. Mostafa, F. Corradi, M. Osswald, F. Stefanini, D. Sumislawska, and G. Indiveri · 2015
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A theory of local learning, the learning channel, and the optimality of backpropagation
P. Baldi and P. Sadowski · 2016
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Deep counter networks for asynchronous event-based processing
J. Binas, G. Indiveri, and M. Pfeiffer · 2016
Earlier work this paper cites.
Convolutional networks for fast, energy-efficient neuromorphic computing
S. K. Esser, P. A. Merolla, J. V. Arthur, A. S. Cassidy, R. Appuswamy, A. Andreopoulos, D. J. Berg, J. L. McKinstry, T. Melano, D. R. Barch, C. di Nolfo, P. Datta, A. Amir, B. Taba, M. D. Flickner, and D. S. Modha · 2016
Earlier work this paper cites.
Training Deep Spiking Neural Networks Using Backpropagation
J. H. Lee, T. Delbruck, and M. Pfeiffer · 2016
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P. O’Connor and M. Welling · 2016
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Event-Driven Random Backpropagation: Enabling Neuromorphic Deep Learning Machines
E. Neftci, C. Augustine, P. Somnath, and G. Detorakis · 2017
Cited alongside, same era.
Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification
B. Rueckauer, I.-A. Lungu, Y. Hu, M. Pfeiffer, and S.-C. Liu · 2017
Cited alongside, same era.
Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights
A. Samadi, T. P. Lillicrap, and D. B. Tweed · 2017
Cited alongside, same era.
S. Yin, S. K. Venkataramanaiah, G. K. Chen, R. Krishnamurthy, Y. Cao, C. Chakrabarti, and J.-s. Seo · 2017
Cited alongside, same era.
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
S. Bartunov, A. Santoro, B. A. Richards, G. E. Hinton, and T. P. Lillicrap · 2018
Cited alongside, same era.
Direct Training for Spiking Neural Networks: Faster, Larger, Better
Y. Wu, L. Deng, G. Li, J. Zhu, and L. Shi · 2018
Later among the works it cites.
Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks
Y. Wu, L. Deng, G. Li, J. Zhu, and L. Shi · 2018
Later among the works it cites.
Superspike: Supervised learning in multilayer spiking neural networks
F. Zenke and S. Ganguli · 2018
Later among the works it cites.
Going Deeper in Spiking Neural Networks: VGG and Residual Architectures
A. Sengupta, Y. Ye, R. Wang, C. Liu, and K. Roy · 2019
Closest in time.
Training deep neural networks for binary communication with the Whetstone method
W. Severa, C. M. Vineyard, R. Dellana, S. J. Verzi, and J. B. Aimone · 2019
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Long short-term memory and learning-to-learn in networks of spiking neurons
G. Bellec, D. Salaj, A. Subramoney, R. Legenstein, and W. Maass · 2018
Cited alongside, same era.
Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks
Y. Jin, P. Li, and W. Zhang · 2018
Cited alongside, same era.
Deep Learning With Spiking Neurons: Opportunities and Challenges
M. Pfeiffer and T. Pfeil · 2018
Cited alongside, same era.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Z. Shuchang, W. Yuxin, N. Zekun, Z. Xinyu, W. He, and Z. Yuheng · 2018
Cited alongside, same era.
Deep learning in spiking neural networks
A. Tavanaei, M. Ghodrati, S. R. Kheradpisheh, T. Masquelier, and A. Maida · 2019
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A Spiking Network for Inference of Relations Trained with Neuromorphic Backpropagation
J. C. Thiele, O. Bichler, A. Dupret, S. Solinas, and G. Indiveri · 2019
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Deep Spiking Neural Network with Spike Count based Learning Rule
J. Wu, Y. Chua, M. Zhang, Q. Yang, G. Li, and H. Li · 2019
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