9 Emerging Hardware Technologies for IoT Data Processing
469
62. A. Shafiee, A. Nag, N. Muralimanohar, R. Balasubramonian, J.P. Strachan, M. Hu, R.S.
Williams, V. Srikumar, ISAAC: a convolutional neural network accelerator with in-situ analog
arithmetic in crossbars. ACM SIGARCH Comput. Archit. News 44(3), 14–26 (2016)
63. P. Chi, S. Li, C. Xu, T. Zhang, J. Zhao, Y. Liu, Y. Wang, Y. Xie, Prime: a novel processing-inmemory architecture for neural network computation in RERAM-based main memory, in In
Proceedings of the 43rd International Symposium on Computer Architecture, 2016
64. M. Gao, Q. Wang, M.T. Arafin, Y. Lyu, G. Qu, Approximate computing for low power and
security in the internet of things. Computer 50(6), 27–34 (2017)
65. Z. Wen, P. Bhatotia, R. Chen, M. Lee and others, ApproxIoT: approximate analytics for edge
computing, in 38th International Conference on Distributed Computing Systems (ICDCS),
2018
66. D. Liu, C. Yang, S. Li, X. Chen, J. Ren, R. Liu, M. Duan, Y. Tan, L. Liang, FitCNN: a cloudassisted and low-cost framework for updating CNNs on IoT devices. Futur. Gener. Comput.
Syst. 91, 277–289 (2019)
67. S. Tajasob, M. Rezaalipour, M. Dehyadegari, M.N. Bojnordi, Designing efficient imprecise
adders using multi-bit approximate building blocks, in Proceedings of the International
Symposium on Low Power Electronics and Design, 2018
68. R. Venkatesan, A. Agarwal, K. Roy, A. Raghunathan, MACACO: modeling and analysis
of circuits for approximate computing, in Proceedings of the International Conference on
Computer-Aided Design, 2011
69. A. Ranjan, S. Venkataramani, X. Fong, K. Roy, A. Raghunathan, Approximate storage for
energy efficient spintronic memories, in In Proc. DAC, 2015
70. D. Mohapatra, V. Chippa, A. Raghunathan, K. Roy, Design of voltage-scalable meta-functions
for approximate computing, in In Proc. DATE, 2011
71. A. Sampson, W. Dietl, E. Fortuna, D. Gnanapragasam, L. Ceze, D. Grossman, EnerJ:
approximate data types for safe and general low-power computation, in in Proc. Int. Conf.
Programm. Lang. Design Implement, 2011
72. H. Esmaeilzadeh, A. Sampson, L. Ceze, D. Burger, Neural acceleration for general-purpose
approximate programs, in In Proceedings of the 45th Annual IEEE/ACM International
Symposium on Microarchitecture, 2012
73. J. Bornholt, T. Mytkowicz, K. McKinley, Uncertain: a first-order type for uncertain data,
in ACM SIGARCH Computer Architecture News, 2014
74. M. Samadi, J. Lee, D. Jamshidi, A. Hormati, S. Mahlke, Sage: self-tuning approximation
for graphics engines, in Annual IEEE/ACM International Symposium on Microarchitecture,
2013
75. W. Baek, T.M. Chilimbi, Green: a framework for supporting energy-conscious programming
using controlled approximation, in in Proc. ACM SIGPLAN Conf. Programm. Lang. Design
Implement, 2010
76. 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, 2014
77. S. Han, J. Pool, J. Tran, W. Dally, Learning both weights and connections for efficient neural
network, in In Advances in Neural Information Processing Systems; Curran Associates, Inc.,
2015
78. S. Han, H. Mao, W. Dally, Deep compression: compressing deep neural networks with
pruning, trained quantization and Huffman coding, in arXiv:1510.00149, 2015
79. Y. Gong, L. Liu, M. Yang, L. Bourdev, Compressing deep convolutional networks using vector
quantization, in arXiv:1412.6115, 2014
80. I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, Y. Bengio, Binarized neural networks, in
In Advances in Neural Information Processing Systems, 2016
81. M. Rastegari, V. Ordonez, J. Redmon, A. Farhadi, XNOR-Net: ImageNet classification using
binary convolutional neural networks, in arXiv:1603.05279, 2016
82. T. Tang, L. Xia, B. Li, Y. Wang, H. Yang, Binary convolutional neural network on RRAM,
in In Proceedings of the 22nd Asia and South Pacific Design Automation Conference (ASPDAC), 2017
469
62. A. Shafiee, A. Nag, N. Muralimanohar, R. Balasubramonian, J.P. Strachan, M. Hu, R.S.
Williams, V. Srikumar, ISAAC: a convolutional neural network accelerator with in-situ analog
arithmetic in crossbars. ACM SIGARCH Comput. Archit. News 44(3), 14–26 (2016)
63. P. Chi, S. Li, C. Xu, T. Zhang, J. Zhao, Y. Liu, Y. Wang, Y. Xie, Prime: a novel processing-inmemory architecture for neural network computation in RERAM-based main memory, in In
Proceedings of the 43rd International Symposium on Computer Architecture, 2016
64. M. Gao, Q. Wang, M.T. Arafin, Y. Lyu, G. Qu, Approximate computing for low power and
security in the internet of things. Computer 50(6), 27–34 (2017)
65. Z. Wen, P. Bhatotia, R. Chen, M. Lee and others, ApproxIoT: approximate analytics for edge
computing, in 38th International Conference on Distributed Computing Systems (ICDCS),
2018
66. D. Liu, C. Yang, S. Li, X. Chen, J. Ren, R. Liu, M. Duan, Y. Tan, L. Liang, FitCNN: a cloudassisted and low-cost framework for updating CNNs on IoT devices. Futur. Gener. Comput.
Syst. 91, 277–289 (2019)
67. S. Tajasob, M. Rezaalipour, M. Dehyadegari, M.N. Bojnordi, Designing efficient imprecise
adders using multi-bit approximate building blocks, in Proceedings of the International
Symposium on Low Power Electronics and Design, 2018
68. R. Venkatesan, A. Agarwal, K. Roy, A. Raghunathan, MACACO: modeling and analysis
of circuits for approximate computing, in Proceedings of the International Conference on
Computer-Aided Design, 2011
69. A. Ranjan, S. Venkataramani, X. Fong, K. Roy, A. Raghunathan, Approximate storage for
energy efficient spintronic memories, in In Proc. DAC, 2015
70. D. Mohapatra, V. Chippa, A. Raghunathan, K. Roy, Design of voltage-scalable meta-functions
for approximate computing, in In Proc. DATE, 2011
71. A. Sampson, W. Dietl, E. Fortuna, D. Gnanapragasam, L. Ceze, D. Grossman, EnerJ:
approximate data types for safe and general low-power computation, in in Proc. Int. Conf.
Programm. Lang. Design Implement, 2011
72. H. Esmaeilzadeh, A. Sampson, L. Ceze, D. Burger, Neural acceleration for general-purpose
approximate programs, in In Proceedings of the 45th Annual IEEE/ACM International
Symposium on Microarchitecture, 2012
73. J. Bornholt, T. Mytkowicz, K. McKinley, Uncertain
in ACM SIGARCH Computer Architecture News, 2014
74. M. Samadi, J. Lee, D. Jamshidi, A. Hormati, S. Mahlke, Sage: self-tuning approximation
for graphics engines, in Annual IEEE/ACM International Symposium on Microarchitecture,
2013
75. W. Baek, T.M. Chilimbi, Green: a framework for supporting energy-conscious programming
using controlled approximation, in in Proc. ACM SIGPLAN Conf. Programm. Lang. Design
Implement, 2010
76. 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, 2014
77. S. Han, J. Pool, J. Tran, W. Dally, Learning both weights and connections for efficient neural
network, in In Advances in Neural Information Processing Systems; Curran Associates, Inc.,
2015
78. S. Han, H. Mao, W. Dally, Deep compression: compressing deep neural networks with
pruning, trained quantization and Huffman coding, in arXiv:1510.00149, 2015
79. Y. Gong, L. Liu, M. Yang, L. Bourdev, Compressing deep convolutional networks using vector
quantization, in arXiv:1412.6115, 2014
80. I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, Y. Bengio, Binarized neural networks, in
In Advances in Neural Information Processing Systems, 2016
81. M. Rastegari, V. Ordonez, J. Redmon, A. Farhadi, XNOR-Net: ImageNet classification using
binary convolutional neural networks, in arXiv:1603.05279, 2016
82. T. Tang, L. Xia, B. Li, Y. Wang, H. Yang, Binary convolutional neural network on RRAM,
in In Proceedings of the 22nd Asia and South Pacific Design Automation Conference (ASPDAC), 2017