publication . Other literature type . Preprint . 2016

Training Spiking Deep Networks for Neuromorphic Hardware

Hunsberger, Eric; Eliasmith, Chris;
Open Access
  • Published: 15 Nov 2016
  • Publisher: Unpublished
Abstract
Comment: 10 pages, 3 figures, 4 tables; the "methods" section of this article draws heavily on arXiv:1510.08829
Subjects
arXiv: Quantitative Biology::Neurons and Cognition
free text keywords: Computer Science - Neural and Evolutionary Computing, Computer Science - Learning
23 references, page 1 of 2

[1] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.

[2] A. Krizhevsky, “Convolutional deep belief networks on CIFAR-10,” Tech. Rep., 2010.

[3] P. Sermanet, S. Chintala, and Y. LeCun, “Convolutional neural networks applied to house numbers digit classification,” in International Conference on Pattern Recognition (ICPR), 2012. [OpenAIRE]

[4] C.-Y. Lee, S. Xie, P. W. Gallagher, Z. Zhang, and Z. Tu, “Deeply-supervised nets,” in International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 38, 2015, pp. 562-570.

[5] R. Gens and P. Domingos, “Discriminative learning of sum-product networks,” in Advances in Neural Information Processing Systems (NIPS), 2012, pp. 1-9.

[6] A. Krizhevsky, I. Sutskever, and G. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, 2012.

[7] C. Eliasmith, T. C. Stewart, X. Choo, T. Bekolay, T. DeWolf, C. Tang, and D. Rasmussen, “A Large-Scale Model of the Functioning Brain,” Science, vol. 338, no. 6111, pp. 1202-1205, Nov. 2012. [OpenAIRE]

[8] E. Neftci, S. Das, B. Pedroni, K. Kreutz-Delgado, and G. Cauwenberghs, “Event-driven contrastive divergence for spiking neuromorphic systems,” Frontiers in Neuroscience, vol. 7, no. 272, 2013.

[9] P. O'Connor, D. Neil, S.-C. Liu, T. Delbruck, and M. Pfeiffer, “Real-time classification and sensor fusion with a spiking deep belief network,” Frontiers in Neuroscience, vol. 7, Jan. 2013.

[10] P. U. Diehl, D. Neil, J. Binas, M. Cook, S.-C. Liu, and M. Pfeiffer, “Fast-Classifying, High-Accuracy Spiking Deep Networks Through Weight and Threshold Balancing,” in IEEE International Joint Conference on Neural Networks (IJCNN), 2015.

[11] Y. Cao, Y. Chen, and D. Khosla, “Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition,” International Journal of Computer Vision, vol. 113, no. 1, pp. 54-66, Nov. 2014.

[12] 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, “Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing,” arXiv preprint, vol. 1603, no. 08270, pp. 1-7, 2016.

[13] P. U. Diehl, G. Zarrella, A. Cassidy, B. U. Pedroni, and E. Neftci, “Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware,” arXiv preprint, vol. 1601, no. 04187, 2016. [OpenAIRE]

[14] B. V. Benjamin, P. Gao, E. McQuinn, S. Choudhary, A. R. Chandrasekaran, J.-M. Bussat, R. AlvarezIcaza, J. V. Arthur, P. A. Merolla, and K. Boahen, “Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations,” Proceedings of the IEEE, vol. 102, no. 5, pp. 699-716, 2014.

[15] E. Hunsberger and C. Eliasmith, “Spiking Deep Networks with LIF Neurons,” arXiv:1510.08829 [cs], pp. 1-9, 2015. [OpenAIRE]

23 references, page 1 of 2
Abstract
Comment: 10 pages, 3 figures, 4 tables; the "methods" section of this article draws heavily on arXiv:1510.08829
Subjects
arXiv: Quantitative Biology::Neurons and Cognition
free text keywords: Computer Science - Neural and Evolutionary Computing, Computer Science - Learning
23 references, page 1 of 2

[1] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.

[2] A. Krizhevsky, “Convolutional deep belief networks on CIFAR-10,” Tech. Rep., 2010.

[3] P. Sermanet, S. Chintala, and Y. LeCun, “Convolutional neural networks applied to house numbers digit classification,” in International Conference on Pattern Recognition (ICPR), 2012. [OpenAIRE]

[4] C.-Y. Lee, S. Xie, P. W. Gallagher, Z. Zhang, and Z. Tu, “Deeply-supervised nets,” in International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 38, 2015, pp. 562-570.

[5] R. Gens and P. Domingos, “Discriminative learning of sum-product networks,” in Advances in Neural Information Processing Systems (NIPS), 2012, pp. 1-9.

[6] A. Krizhevsky, I. Sutskever, and G. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, 2012.

[7] C. Eliasmith, T. C. Stewart, X. Choo, T. Bekolay, T. DeWolf, C. Tang, and D. Rasmussen, “A Large-Scale Model of the Functioning Brain,” Science, vol. 338, no. 6111, pp. 1202-1205, Nov. 2012. [OpenAIRE]

[8] E. Neftci, S. Das, B. Pedroni, K. Kreutz-Delgado, and G. Cauwenberghs, “Event-driven contrastive divergence for spiking neuromorphic systems,” Frontiers in Neuroscience, vol. 7, no. 272, 2013.

[9] P. O'Connor, D. Neil, S.-C. Liu, T. Delbruck, and M. Pfeiffer, “Real-time classification and sensor fusion with a spiking deep belief network,” Frontiers in Neuroscience, vol. 7, Jan. 2013.

[10] P. U. Diehl, D. Neil, J. Binas, M. Cook, S.-C. Liu, and M. Pfeiffer, “Fast-Classifying, High-Accuracy Spiking Deep Networks Through Weight and Threshold Balancing,” in IEEE International Joint Conference on Neural Networks (IJCNN), 2015.

[11] Y. Cao, Y. Chen, and D. Khosla, “Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition,” International Journal of Computer Vision, vol. 113, no. 1, pp. 54-66, Nov. 2014.

[12] 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, “Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing,” arXiv preprint, vol. 1603, no. 08270, pp. 1-7, 2016.

[13] P. U. Diehl, G. Zarrella, A. Cassidy, B. U. Pedroni, and E. Neftci, “Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware,” arXiv preprint, vol. 1601, no. 04187, 2016. [OpenAIRE]

[14] B. V. Benjamin, P. Gao, E. McQuinn, S. Choudhary, A. R. Chandrasekaran, J.-M. Bussat, R. AlvarezIcaza, J. V. Arthur, P. A. Merolla, and K. Boahen, “Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations,” Proceedings of the IEEE, vol. 102, no. 5, pp. 699-716, 2014.

[15] E. Hunsberger and C. Eliasmith, “Spiking Deep Networks with LIF Neurons,” arXiv:1510.08829 [cs], pp. 1-9, 2015. [OpenAIRE]

23 references, page 1 of 2
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