468
M. N. Bojnordi and P. Behnam
41. M. Zidan, H. Fahmy, M. Hussain, K. Salama, Memristor-based memory: the sneak paths
problem and solutions. Microelectron. J. 44(2), 176–183 (2013)
42. Y. Taigman, M. Yang, M. Ranzato, L. Wolf, DeepFace: closing the gap to human-level
performance in face verification, in In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 2014
43. A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional
neural networks, in Advances In Neural Information Processing Systems, 2012
44. X. Lei, A.W. Senior, A. Gruenstein, J. Sorensen, Accurate and compact large vocabulary
speech recognition on mobile devices, in Interspeech, 2013
45. M. Motamedi, D. Fong, S. Ghiasi, Fast and energy-efficient CNN inference on IoT devices,
arXiv preprint arXiv:1611.07151, 2016
46. L. Oskouei, S.G.H. Salar, M. Hashemi, S. Ghiasi, Cnndroid: Gpu-accelerated execution
of trained deep convolutional neural networks on android, in Proceedings of the ACM on
Multimedia Conference, 2016
47. S. Mehta, J. Torrellas, WearCore: a core for wearable workloads?, in International Conference
on Parallel Architecture and Compilation Techniques (PACT), 2016
48. K. Ma, X. Li, K. Swaminathan, Y. Zheng, S. Li, Y. Liu, Y. Xie, J.J. Sampson, V. Narayanan,
Nonvolatile processor architectures: Efficient, reliable progress with unstable power. IEEE
Micro 36(3), 72–83 (2016)
49. Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, L. Jackel, Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989)
50. S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. Horowitz, W. Dally, EIE: efficient inference
engine on compressed deep neural network, in Proceedings of the 43rd International
Symposium on Computer Architecture (ISCA), 2016
51. E. Denton, W. Zaremba, J. Bruna, Y. LeCun, R. Fergus, Exploiting linear structure within
convolutional networks for efficient evaluation, in In Advances in Neural Information
Processing Systems; Curran Associates, Inc., 2014
52. C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, J. Cong, Optimizing FPGA-based accelerator
design for deep convolutional neural networks, in In Proceedings of the ACM/SIGDA
International Symposium on Field-Programmable Gate Arrays, 2015
53. F. Iandola, S. Han, M. Moskewicz, K. Ashraf, W. Dally, K. Keutzer, SqueezeNet: AlexNetlevel accuracy with 50× fewer parameters and <0.5 MB model size, in arXiv:1602.07360,
2016
54. W. Chen, J. Wilson, S. Tyree, K. Weinberger, Y. Chen, Compressing neural networks with the
Hashing Trick, in In Proceedings of the ICML, 2015
55. T. Chen, Z. Du, N. Sun, J. Wang, C. Wu, Y. Chen, O.D. Temam, Diannao: a small-footprint
high-throughput accelerator for ubiquitous machine-learning, in In ACM Sigplan Notices,
2014
56. Y. Chen, J. Emer, V. Sze, Eyeriss: a spatial architecture for energy-efficient dataflow for
convolutional neural networks, in In Proceedings of 43rd the ACM/IEEE Annual International
Symposium on Computer Architecture (ISCA), 2016
57. Z. Du, R. Fasthuber, T. Chen, P. Ienne, L. Li, T. Luo, X. Feng, Y. Chen, O. S. Temam,
ShiDianNao: shifting vision processing closer to the sensor, in In ACM SIGARCH Computer
Architecture News, 2015
58. R. Kozma, R.E. Pino, G.E. Pazienza, Advances in Neuromorphic Memristor Science and
Applications (Springer Publishing Company, 2012)
59. A.M. Sheri, A. Rafique, W. Pedrycz, M. Jeon, Contrastive divergence for memristor-based
restricted Boltzmann machine. Eng. Appl. Artif. Intell. 37, 336–342 (2015)
60. M. Prezioso, F. Merrikh-Bayat, B. Hoskins, G. Adam, K.K. Likharev, D.B. Strukov, Training
and operation of an integrated 12 neuromorphic network based on metal-oxide memristors.
Nature 521(7550), 61–64 (2015)
61. M.N. Bojnordi, E. Ipek, Memristive Boltzmann machine: a hardware accelerator for combinatorial optimization and deep learning, in IEEE International Symposium on High Performance
Computer Architecture (HPCA), (IEEE, Barcelona, 2016)
M. N. Bojnordi and P. Behnam
41. M. Zidan, H. Fahmy, M. Hussain, K. Salama, Memristor-based memory: the sneak paths
problem and solutions. Microelectron. J. 44(2), 176–183 (2013)
42. Y. Taigman, M. Yang, M. Ranzato, L. Wolf, DeepFace: closing the gap to human-level
performance in face verification, in In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 2014
43. A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional
neural networks, in Advances In Neural Information Processing Systems, 2012
44. X. Lei, A.W. Senior, A. Gruenstein, J. Sorensen, Accurate and compact large vocabulary
speech recognition on mobile devices, in Interspeech, 2013
45. M. Motamedi, D. Fong, S. Ghiasi, Fast and energy-efficient CNN inference on IoT devices,
arXiv preprint arXiv:1611.07151, 2016
46. L. Oskouei, S.G.H. Salar, M. Hashemi, S. Ghiasi, Cnndroid: Gpu-accelerated execution
of trained deep convolutional neural networks on android, in Proceedings of the ACM on
Multimedia Conference, 2016
47. S. Mehta, J. Torrellas, WearCore: a core for wearable workloads?, in International Conference
on Parallel Architecture and Compilation Techniques (PACT), 2016
48. K. Ma, X. Li, K. Swaminathan, Y. Zheng, S. Li, Y. Liu, Y. Xie, J.J. Sampson, V. Narayanan,
Nonvolatile processor architectures: Efficient, reliable progress with unstable power. IEEE
Micro 36(3), 72–83 (2016)
49. Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, L. Jackel, Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989)
50. S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. Horowitz, W. Dally, EIE: efficient inference
engine on compressed deep neural network, in Proceedings of the 43rd International
Symposium on Computer Architecture (ISCA), 2016
51. E. Denton, W. Zaremba, J. Bruna, Y. LeCun, R. Fergus, Exploiting linear structure within
convolutional networks for efficient evaluation, in In Advances in Neural Information
Processing Systems; Curran Associates, Inc., 2014
52. C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, J. Cong, Optimizing FPGA-based accelerator
design for deep convolutional neural networks, in In Proceedings of the ACM/SIGDA
International Symposium on Field-Programmable Gate Arrays, 2015
53. F. Iandola, S. Han, M. Moskewicz, K. Ashraf, W. Dally, K. Keutzer, SqueezeNet: AlexNetlevel accuracy with 50× fewer parameters and <0.5 MB model size, in arXiv:1602.07360,
2016
54. W. Chen, J. Wilson, S. Tyree, K. Weinberger, Y. Chen, Compressing neural networks with the
Hashing Trick, in In Proceedings of the ICML, 2015
55. T. Chen, Z. Du, N. Sun, J. Wang, C. Wu, Y. Chen, O.D. Temam, Diannao: a small-footprint
high-throughput accelerator for ubiquitous machine-learning, in In ACM Sigplan Notices,
2014
56. Y. Chen, J. Emer, V. Sze, Eyeriss: a spatial architecture for energy-efficient dataflow for
convolutional neural networks, in In Proceedings of 43rd the ACM/IEEE Annual International
Symposium on Computer Architecture (ISCA), 2016
57. Z. Du, R. Fasthuber, T. Chen, P. Ienne, L. Li, T. Luo, X. Feng, Y. Chen, O. S. Temam,
ShiDianNao: shifting vision processing closer to the sensor, in In ACM SIGARCH Computer
Architecture News, 2015
58. R. Kozma, R.E. Pino, G.E. Pazienza, Advances in Neuromorphic Memristor Science and
Applications (Springer Publishing Company, 2012)
59. A.M. Sheri, A. Rafique, W. Pedrycz, M. Jeon, Contrastive divergence for memristor-based
restricted Boltzmann machine. Eng. Appl. Artif. Intell. 37, 336–342 (2015)
60. M. Prezioso, F. Merrikh-Bayat, B. Hoskins, G. Adam, K.K. Likharev, D.B. Strukov, Training
and operation of an integrated 12 neuromorphic network based on metal-oxide memristors.
Nature 521(7550), 61–64 (2015)
61. M.N. Bojnordi, E. Ipek, Memristive Boltzmann machine: a hardware accelerator for combinatorial optimization and deep learning, in IEEE International Symposium on High Performance
Computer Architecture (HPCA), (IEEE, Barcelona, 2016)