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Fully realizing the potential of acceleration for Deep Neural Networks (DNNs) requires understanding and leveraging algorithmic properties.
M. P. Marcus, M. A. Marcinkiewicz, and B. Santorini, “Building a large annotated corpus of english: The penn treebank,” Computational linguistics
1993
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation
1997
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE
1998
Earlier work this paper cites.
E. Ipek, M. Kirman, N. Kirman, and J. F. Martinez, “Core fusion: accommodating software diversity in chip multiprocessors,” in ISCA
2007
Earlier work this paper cites.
C. Kim, S. Sethumadhavan, M. Govindan, N. Ranganathan, D. Gulati, D. Burger, and S. W. Keckler, “Composable lightweight processors,” in MICRO
2007
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Computer Science Department, University of Toronto, Tech. Rep
2009
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” in NIPS workshop on deep learning and unsupervised feature learning
2011
Earlier work this paper cites.
S. Li, K. Chen, J. H. Ahn, J. B. Brockman, and N. P. Jouppi, “CACTI-P: Architecture-level Modeling for SRAM-based Structures with Advanced Leakage Reduction Techniques,” in ICCAD
2011
Earlier work this paper cites.
H. Esmaeilzadeh, E. Blem, R. St. Amant, K. Sankaralingam, and D. Burger, “Dark silicon and the end of multicore scaling,” in ISCA
2011
Earlier work this paper cites.
Y. Chen, T. Luo, S. Liu, S. Zhang, L. He, J. Wang, L. Li, T. Chen, Z. Xu, N. Sun, et al
2014
Earlier work this paper cites.
T. Chen, Z. Du, N. Sun, J. Wang, C. Wu, Y. Chen, and O. Temam, “Diannao: a small-footprint high-throughput accelerator for ubiquitous machine-learning,” in ASPLOS
2014
Earlier work this paper cites.
V. Gokhale, J. Jin, A. Dundar, B. Martini, and E. Culurciello, “A 240 g-ops/s mobile coprocessor for deep neural networks,” in CVPRW
2014
Earlier work this paper cites.
A. Krizhevsky, “One weird trick for parallelizing convolutional neural networks,” arXiv
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv
2014
Earlier work this paper cites.
D. Liu, T. Chen, S. Liu, J. Zhou, S. Zhou, O. Teman, X. Feng, X. Zhou, and Y. Chen, “Pudiannao: A polyvalent machine learning accelerator,” in ASPLOS
2015
Earlier work this paper cites.
Z. Du, R. Fasthuber, T. Chen, P. Ienne, L. Li, T. Luo, X. Feng, Y. Chen, and O. Temam, “Shidiannao: shifting vision processing closer to the sensor,” in ISCA
2015
Earlier work this paper cites.
F. Conti and L. Benini, “A ultra-low-energy convolution engine for fast brain-inspired vision in multicore clusters,” in DATE
2015
Earlier work this paper cites.
C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, and J. Cong, “Optimizing fpga-based accelerator design for deep convolutional neural networks,” in FPGA
2015
Earlier work this paper cites.
Y.-H. Chen, J. Emer, and V. Sze, “Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks,” in ISCA
2016
Earlier work this paper cites.
P. Judd, J. Albericio, T. Hetherington, T. M. Aamodt, and A. Moshovos, “Stripes: Bit-serial deep neural network computing,” in MICRO
2016
Earlier work this paper cites.
S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally, “Eie: efficient inference engine on compressed deep neural network,” in ISCA
2016
Earlier work this paper cites.
D. Kim, J. Kung, S. Chai, S. Yalamanchili, and S. Mukhopadhyay, “Neurocube: A programmable digital neuromorphic architecture with high-density 3d memory,” in ISCA
2016
Cited alongside, same era.
B. Reagen, P. Whatmough, R. Adolf, S. Rama, H. Lee, S. K. Lee, J. M. Hernández-Lobato, G.-Y. Wei, and D. Brooks, “Minerva: Enabling low-power, highly-accurate deep neural network accelerators,” in ISCA
2016
Cited alongside, same era.
J. Albericio, P. Judd, T. Hetherington, T. Aamodt, N. E. Jerger, and A. Moshovos, “Cnvlutin: ineffectual-neuron-free deep neural network computing,” in ISCA
2016
Cited alongside, same era.
S. Liu, Z. Du, J. Tao, D. Han, T. Luo, Y. Xie, Y. Chen, and T. Chen, “Cambricon: An instruction set architecture for neural networks,” in ISCA
2016
Cited alongside, same era.
S. Zhang, Z. Du, L. Zhang, H. Lan, S. Liu, L. Li, Q. Guo, T. Chen, and Y. Chen, “Cambricon-x: An accelerator for sparse neural networks,” in MICRO
M. Alwani, H. Chen, M. Ferdman, and P. Milder, “Fused-layer cnn accelerator,” in MICRO
2016
Later among the works it cites.
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, “Xnor-net: Imagenet classification using binary convolutional neural networks,” arXiv
2016
Later among the works it cites.
Y.-H. Chen, T. Krishna, J. S. Emer, and V. Sze, “Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks,” JSSC
2017
Closest in time.
M. Gao, J. Pu, X. Yang, M. Horowitz, and C. Kozyrakis, “Tetris: Scalable and efficient neural network acceleration with 3d memory,” in ASPLOS
2017
Closest in time.
A. Delmas, S. Sharify, P. Judd, and A. Moshovos, “Tartan: Accelerating fully-connected and convolutional layers in deep learning networks by exploiting numerical precision variability,” arXiv
2017
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2016
Cited alongside, same era.
J. Sim, J. S. Park, M. Kim, D. Bae, Y. Choi, and L. S. Kim, “14.6 a 1.42tops/w deep convolutional neural network recognition processor for intelligent ioe systems,” in ISSCC
2016
Cited alongside, same era.
Y. Wang, J. Xu, Y. Han, H. Li, and X. Li, “Deepburning: Automatic generation of fpga-based learning accelerators for the neural network family,” in DAC
2016
Cited alongside, same era.
L. Song, Y. Wang, Y. Han, X. Zhao, B. Liu, and X. Li, “C-brain: A deep learning accelerator that tames the diversity of cnns through adaptive data-level parallelization,” in DAC
2016
Cited alongside, same era.
H. Sharma, J. Park, D. Mahajan, E. Amaro, J. K. Kim, C. Shao, A. Misra, and H. Esmaeilzadeh, “From high-level deep neural models to fpgas,” in MICRO
2016
Cited alongside, same era.
M. Alwani, H. Chen, M. Ferdman, and P. Milder, “Fused-layer cnn accelerators,” in MICRO
2016
Cited alongside, same era.
N. Suda, V. Chandra, G. Dasika, A. Mohanty, Y. Ma, S. Vrudhula, J.-s. Seo, and Y. Cao, “Throughput-optimized opencl-based fpga accelerator for large-scale convolutional neural networks,” in FPGA
2016
Cited alongside, same era.
J. Qiu, J. Wang, S. Yao, K. Guo, B. Li, E. Zhou, J. Yu, T. Tang, N. Xu, S. Song, et al
2016
Cited alongside, same era.
Closest in time.
L. Song, X. Qian, H. Li, and Y. Chen, “Pipelayer: A pipelined reram-based accelerator for deep learning,” in HPCA
2017
Closest in time.
E. Chung, J. Fowers, K. Ovtcharov, M. Papamichael, A. Caulfield, T. Massengil, M. Liu, D. Lo, S. Alkalay, M. Haselman, C. Boehn, O. Firestein, A. Forin, K. S. Gatlin, M. Ghandi, S. Heil, K. Holohan, T. Juhasz, R. K. Kovvuri, S. Lanka, F. van Megen, D. Mukhortov, P. Patel, S. Reinhardt, A. Sapek, R. Seera, B. Sridharan, L. Woods, P. Yi-Xiao, R. Zhao, and D. Burger, “Accelerating persistent neural networks at datacenter scale,” in HotChips
2017
Closest in time.
N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers, et al
2017
Closest in time.
A. K. Mishra, E. Nurvitadhi, J. J. Cook, and D. Marr, “WRPN: wide reduced-precision networks,” arXiv
2017
Closest in time.
B. Moons, R. Uytterhoeven, W. Dehaene, and M. Verhelst, “Dvafs: Trading computational accuracy for energy through dynamic-voltage-accuracy-frequency-scaling,” in DATE
2017
Closest in time.
S. Sharify, A. D. Lascorz, P. Judd, and A. Moshovos, “Loom: Exploiting weight and activation precisions to accelerate convolutional neural networks,” arXiv
2017
Closest in time.
T. Rzayev, S. Moradi, D. H. Albonesi, and R. Manohar, “Deeprecon: Dynamically reconfigurable architecture for accelerating deep neural networks,” IJCNN
2017
Closest in time.
Y. Umuroglu, N. J. Fraser, G. Gambardella, M. Blott, P. Leong, M. Jahre, and K. Vissers, “Finn: A framework for fast, scalable binarized neural network inference,” in FPGA
2017
Closest in time.
K. Ando, K. Ueyoshi, K. Orimo, H. Yonekawa, S. Sato, H. Nakahara, M. Ikebe, T. Asai, S. Takamaeda-Yamazaki, T. Kuroda, et al
2017
Closest in time.
H. Kim, J. Sim, Y. Choi, and L.-S. Kim, “A kernel decomposition architecture for binary-weight convolutional neural networks,” in DAC
2017
Closest in time.
A. Parashar, M. Rhu, A. Mukkara, A. Puglielli, R. Venkatesan, B. Khailany, J. Emer, S. W. Keckler, and W. J. Dally, “SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks,” in ISCA
2017
Closest in time.
Y. Shen, M. Ferdman, and P. Milder, “Escher: A cnn accelerator with flexible buffering to minimize off-chip transfer,” in FCCM
2017
Closest in time.
J. Lee, C. Kim, S. Kang, D. Shin, S. Kim, and H.-J. Yoo, “Unpu: A 50.6 tops/w unified deep neural network accelerator with 1b-to-16b fully-variable weight bit-precision,” in ISSCC
2018
Closest in time.
A. Yazdanbakhsh, H. Falahati, P. J. Wolfe, K. Samadi, H. Esmaeilzadeh, and N. S. Kim, “GANAX: A Unified SIMD-MIMD Acceleration for Generative Adversarial Network,” in ISCA
2018
Closest in time.
V. Aklaghi, A. Yazdanbakhsh, K. Samadi, H. Esmaeilzadeh, and R. K. Gupta, “Snapea: Predictive early activation for reducing computation in deep convolutional neural networks,” in ISCA
2018
Closest in time.