O. Temam, The rebirth of neural networks, Keynote speach at the International Symposium on Computer Architecture, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00535554

C. Farabet, B. Martini, P. Akselrod, S. Talay, Y. Lecun et al., Hardware accelerated convolutional neural networks for synthetic vision systems, Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp.257-260, 2010.
DOI : 10.1109/ISCAS.2010.5537908

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.184.3162

S. Han, X. Liu, H. Mao, J. Pu, A. Pedram et al., EIE, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture, 2016.
DOI : 10.1109/FCCM.2014.23

S. K. Esser, R. Appuswamy, P. Merolla, J. V. Arthur, and D. S. Modha, Backpropagation for energy-efficient neuromorphic computing, Advances in Neural Information Processing Systems, pp.1117-1125, 2015.
DOI : 10.1073/pnas.1604850113

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5068316

M. S. Razlighi, M. Imani, F. Koushanfar, and T. Rosing, LookNN: Neural network with no multiplication, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017, pp.1775-1780, 2017.
DOI : 10.23919/DATE.2017.7927280

K. Asanovic and N. Morgan, Experimental determination of precision requirements for back-propagation training of artificial neural networks, Proceedings 2nd International Conference on Microelectronics for Neural Networks, 1991.

M. Courbariaux, Y. Bengio, and J. David, Binaryconnect: Training deep neural networks with binary weights during propagations, Advances in Neural Information Processing Systems, pp.3123-3131, 2015.

M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks, European Conference on Computer Vision, pp.525-542, 2016.
DOI : 10.1103/PhysRevLett.115.128101

URL : http://arxiv.org/abs/1603.05279

H. Alemdar, V. Leroy, A. Prost-boucle, and F. Pétrot, Ternary neural networks for resource-efficient AI applications, 2017 International Joint Conference on Neural Networks (IJCNN), 2017.
DOI : 10.1109/IJCNN.2017.7966166

URL : https://hal.archives-ouvertes.fr/hal-01481478

R. Andri, L. Cavigelli, D. Rossi, and L. Benini, YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights, 2016 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), pp.236-241, 2016.
DOI : 10.1109/ISVLSI.2016.111

URL : http://arxiv.org/abs/1606.05487

Y. Umuroglu, N. J. Fraser, G. Gambardella, M. Blott, P. H. Leong et al., FINN, Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA '17, 1612.
DOI : 10.1145/1498765.1498785

T. Chiueh and R. M. Goodman, Learning algorithms for neural networks with ternary weights, Neural Networks, vol.1, p.166, 1988.
DOI : 10.1016/0893-6080(88)90203-1

B. Hoskins, M. Haskard, and G. Curkowicz, A VLSI implementation of multi-layer neural network with ternary activation functions and limited integer weights, Proceedings of International Conference on Microelectronics, pp.843-846, 1995.
DOI : 10.1109/ICMEL.1995.500978

P. ?koda, T. Lipi´clipi´c, Ã. Srp, B. M. Rogina, K. Skala et al., Implementation framework for artificial neural networks on fpga, 2011 Proceedings of the 34th International Convention MIPRO, pp.274-278, 2011.

M. Jacobsen, D. Richmond, M. Hogains, and R. Kastner, RIFFA 2.1, ACM Transactions on Reconfigurable Technology and Systems, vol.8, issue.4, 2015.
DOI : 10.1109/FPL.2006.311336

A. Krizhevsky, Learning multiple layers of features from tiny images, 2009.

J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition, International Joint Conference on Neural Networks, 2011.
DOI : 10.1016/j.neunet.2012.02.016

Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu et al., Reading digits in natural images with unsupervised feature learning, NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011.

Y. Li, Z. Liu, K. Xu, H. Yu, and F. Ren, A 7.663-tops 8.2-w energyefficient fpga accelerator for binary convolutional neural networks, 2017.
URL : https://hal.archives-ouvertes.fr/pasteur-00823211

R. Zhao, W. Song, W. Zhang, T. Xing, J. Lin et al., Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs, Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA '17, pp.15-24, 2017.
DOI : 10.1145/2897937.2898003