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We introduce a principled method to train end-to-end analog neural networks by stochastic gradient descent.
Memristor-the missing circuit element
L. Chua · 1971
Earlier work this paper cites.
Neural networks and physical systems with emergent collective computational abilities
J. J. Hopfield · 1982
Earlier work this paper cites.
Absolute stability of global pattern formation and parallel memory storage by competitive neural networks
M. A. Cohen and S. Grossberg · 1983
Earlier work this paper cites.
Neurons with graded response have collective computational properties like those of two-state neurons
J. J. Hopfield · 1984
Earlier work this paper cites.
A learning algorithm for boltzmann machines
D. H. Ackley, G. E. Hinton, and T. J. Sejnowski · 1985
Earlier work this paper cites.
Analog vlsi and neutral systems
C. Mead · 1989
Earlier work this paper cites.
Contrastive hebbian learning in the continuous hopfield model
J. R. Movellan · 1991
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
A tutorial on energy-based learning
Y. LeCun, S. Chopra, R. Hadsell, M. Ranzato, and F. Huang · 2006
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
Nanoscale memristor device as synapse in neuromorphic systems
S. H. Jo, T. Chang, I. Ebong, B. B. Bhadviya, P. Mazumder, and W. Lu · 2010
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al · 2012
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
A compositional framework for passive linear networks
J. C. Baez and B. Fong · 2015
Earlier work this paper cites.
Merging the interface: Power, area and accuracy co-optimization for rram crossbar-based mixed-signal computing system
B. Li, L. Xia, P. Gu, Y. Wang, and H. Yang · 2015
Earlier work this paper cites.
Spin-transfer torque magnetic memory as a stochastic memristive synapse for neuromorphic systems
A. F. Vincent, J. Larroque, N. Locatelli, N. Ben Romdhane, O. Bichler, C. Gamrat, W. S. Zhao, J. Klein, S. Galdin-Retailleau, and D. Querlioz · 2015
Earlier work this paper cites.
Unsupervised learning by spike timing dependent plasticity in phase change memory (pcm) synapses
S. Ambrogio, N. Ciocchini, M. Laudato, V. Milo, A. Pirovano, P. Fantini, and D. Ielmini · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Dot-product engine for neuromorphic computing: Programming 1t1m crossbar to accelerate matrix-vector multiplication
M. Hu, J. P. Strachan, Z. Li, E. M. Grafals, N. Davila, C. Graves, S. Lam, N. Ge, J. J. Yang, and R. S. Williams · 2016
Cited alongside, same era.
Repeatable, accurate, and high speed multi-level programming of memristor 1t1r arrays for power efficient analog computing applications
E. J. Merced-Grafals, N. Dávila, N. Ge, R. S. Williams, and J. P. Strachan · 2016
Cited alongside, same era.
Wavenet: A generative model for raw audio
A. v. d. Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu · 2016
Cited alongside, same era.
Neuromorphic computing using non-volatile memory
G. W. Burr, R. M. Shelby, A. Sebastian, S. Kim, S. Kim, S. Sidler, K. Virwani, M. Ishii, P. Narayanan, A. Fumarola, et al · 2017
Cited alongside, same era.
Mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse
Training a spiking neural network with equilibrium propagation
P. O’Connor, E. Gavves, and M. Welling · 2019
Later among the works it cites.
An mram-based deep in-memory architecture for deep neural networks
A. D. Patil, H. Hua, S. Gonugondla, M. Kang, and N. R. Shanbhag · 2019
Later among the works it cites.
Analog/mixed-signal hardware error modeling for deep learning inference
A. S. Rekhi, B. Zimmer, N. Nedovic, N. Liu, R. Venkatesan, M. Wang, B. Khailany, W. J. Dally, and C. T. Gray · 2019
Later among the works it cites.
A deep learning framework for neuroscience
B. A. Richards, T. P. Lillicrap, P. Beaudoin, Y. Bengio, R. Bogacz, A. Christensen, C. Clopath, R. P. Costa, A. de Berker, S. Ganguli, et al · 2019
Later among the works it cites.
Equivalence of equilibrium propagation and recurrent backpropagation
B. Scellier and Y. Bengio · 2019
Later among the works it cites.
Reinforcement learning with analogue memristor arrays
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Fast, energy-efficient, robust, and reproducible mixed-signal neuromorphic classifier based on embedded nor flash memory technology
X. Guo, F. M. Bayat, M. Bavandpour, M. Klachko, M. Mahmoodi, M. Prezioso, K. Likharev, and D. Strukov · 2017
Cited alongside, same era.
Ferroelectric fet analog synapse for acceleration of deep neural network training
M. Jerry, P.-Y. Chen, J. Zhang, P. Sharma, K. Ni, S. Yu, and S. Datta · 2017
Cited alongside, same era.
Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
B. Scellier and Y. Bengio · 2017
Cited alongside, same era.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Equivalent-accuracy accelerated neural-network training using analogue memory
S. Ambrogio, P. Narayanan, H. Tsai, R. M. Shelby, I. Boybat, C. di Nolfo, S. Sidler, M. Giordano, M. Bodini, N. C. Farinha, et al · 2018
Cited alongside, same era.
Bidirectional learning in recurrent neural networks using equilibrium propagation
A. F. Khan · 2018
Cited alongside, same era.
Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
C. Li, D. Belkin, Y. Li, P. Yan, M. Hu, N. Ge, H. Jiang, E. Montgomery, P. Lin, Z. Wang, et al · 2018
Cited alongside, same era.
Z. Wang, C. Li, W. Song, M. Rao, D. Belkin, Y. Li, P. Yan, H. Jiang, P. Lin, M. Hu, et al · 2019
Later among the works it cites.
Theories of error back-propagation in the brain
J. C. Whittington and R. Bogacz · 2019
Later among the works it cites.
Memristive crossbar arrays for brain-inspired computing
Q. Xia and J. J. Yang · 2019
Later among the works it cites.
Spectre circuit simulator reference, version 19.1, Jan 2020
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The dirichlet problem on directed networks
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Nonlinear electrical networks, 2010
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