Fetching the paper…
Reading the bibliography…
We show that selecting a single data type (precision) for all values in Deep Neural Networks, even if that data type is different per layer, amounts to worst case design.
S. White, “Applications of distributed arithmetic to digital signal processing: a tutorial review,” IEEE ASSP Magazine
1989
Earlier work this paper cites.
T. Xanthopoulos and A. P. Chandrakasan, “A low-power dct core using adaptive bitwidth and arithmetic activity exploiting signal correlations and quantization,” IEEE Journal of Solid-State Circuits
2000
Earlier work this paper cites.
S. Roth and M. J. Black, “Fields of experts: a framework for learning image priors,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)
2005
Earlier work this paper cites.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising with block-matching and 3D filtering,” in Electronic Imaging 2006
2006
Earlier work this paper cites.
G. J. Brostow, J. Fauqueur, and R. Cipolla, “Semantic object classes in video: A high-definition ground truth database,” Pattern Recognition Letters
2008
Earlier work this paper cites.
C. Rashtchian, P. Young, M. Hodosh, and J. Hockenmaier, “Collecting image annotations using amazon’s mechanical turk,” in Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk
2010
Earlier work this paper cites.
M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel, “Low-complexity single-image super-resolution based on nonnegative neighbor embedding,” 2012
2012
Earlier work this paper cites.
R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse-representations,” in Curves and Surfaces
2012
Earlier work this paper cites.
H. C. Burger, C. J. Schuler, and S. Harmeling, “Image denoising: Can plain neural networks compete with bm3d?,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition
2012
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 Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
2014
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, and O. Temam, “Dadiannao: A machine-learning supercomputer,” in Microarchitecture (MICRO), 2014 47th Annual IEEE/ACM International Symposium on
2014
Earlier work this paper cites.
J. Kim, K. Hwang, and W. Sung, “X1000 real-time phoneme recognition VLSI using feed-forward deep neural networks,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2014
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” arXiv:1409.0575 [cs] · 2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
A. Karpathy and F. Li, “Deep visual-semantic alignments for generating image descriptions,” CoRR
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Courbariaux, Y. Bengio, and J.-P. David, “BinaryConnect: Training Deep Neural Networks with binary weights during propagations,” ArXiv e-prints
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” CoRR
2015
Earlier work this paper cites.
M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge: A retrospective,” International Journal of Computer Vision
2015
Earlier work this paper cites.
J. Donahue, L. A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, “Long-term recurrent convolutional networks for visual recognition and description,” in CVPR
2015
Cited alongside, same era.
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in 2015 IEEE International Conference on Computer Vision (ICCV)
2015
Cited alongside, same era.
Y. Jia, “Caffe model zoo,” https://github.com/BVLC/caffe/wiki/Model-Zoo
2015
Cited alongside, same era.
M. Poremba, S. Mittal, D. Li, J. Vetter, and Y. Xie, “Destiny: A tool for modeling emerging 3d nvm and edram caches,” in Design, Automation Test in Europe Conference Exhibition (DATE), 2015
J. Albericio, A. Delmás, P. Judd, S. Sharify, G. O’Leary, R. Genov, and A. Moshovos, “Bit-pragmatic deep neural network computing,” in Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture
2017
Later among the works it cites.
M. Zhu and S. Gupta, “To prune, or not to prune: exploring the efficacy of pruning for model compression,” ArXiv e-prints
2017
Later among the works it cites.
2017
Later among the works it cites.
B. Moons, R. Uytterhoeven, W. Dehaene, and M. Verhelst, “Envision: A 0.26-to-10tops/w subword-parallel dynamic-voltage-accuracy-frequency-scalable convolutional neural network processor in 28nm fdsoi,” in IEEE Solid-State Circuits Conference (ISSCC)
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
M. Courbariaux, Y. Bengio, and J.-P. David, “Binaryconnect: Training deep neural networks with binary weights during propagations,” in Advances in Neural Information Processing Systems
2015
Cited alongside, same era.
Software available from tensorflow.org
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015 · 2015
Cited alongside, same era.
Chen, Yu-Hsin and Krishna, Tushar and Emer, Joel and Sze, Vivienne, “Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks,” in IEEE International Solid-State Circuits Conference, ISSCC 2016, Digest of Technical Papers
2016
Cited alongside, same era.
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,” arXiv:1602.01528 [cs] · 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 2016 IEEE/ACM International Conference on Computer Architecture (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 Proceedings of the 49th International Symposium on Microarchitecture
2016
Cited alongside, same era.
P. Warden, “Low-precision matrix multiplication.” https://petewarden.com , 2016
2016
Cited alongside, same era.
N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers, R. Boyle, P.-l. Cantin, C. Chao, C. Clark, J. Coriell, M. Daley, M. Dau, J. Dean, B. Gelb, T. V. Ghaemmaghami, R. Gottipati, W. Gulland, R. Hagmann, C. R. Ho, D. Hogberg, J. Hu, R. Hundt, D. Hurt, J. Ibarz, A. Jaffey, A. Jaworski, A. Kaplan, H. Khaitan, D. Killebrew, A. Koch, N. Kumar, S. Lacy, J. Laudon, J. Law, D. Le, C. Leary, Z. Liu, K. Lucke, A. Lundin, G. MacKean, A. Maggiore, M. Mahony, K. Miller, R. Nagarajan, R. Narayanaswami, R. Ni, K. Nix, T. Norrie, M. Omernick, N. Penukonda, A. Phelps, J. Ross, M. Ross, A. Salek, E. Samadiani, C. Severn, G. Sizikov, M. Snelham, J. Souter, D. Steinberg, A. Swing, M. Tan, G. Thorson, B. Tian, H. Toma, E. Tuttle, V. Vasudevan, R. Walter, W. Wang, E. Wilcox, and D. H. Yoon, “In-datacenter performance analysis of a tensor processing unit,” in Proceedings of the 44th Annual International Symposium on Computer Architecture
2017
Later among the works it cites.
J. Park, S. Li, W. Wen, P. T. P. Tang, H. Li, Y. Chen, and P. Dubey, “Faster CNNs with Direct Sparse Convolutions and Guided Pruning,” in 5th International Conference on Learning Representations (ICLR)
2017
Later among the works it cites.
Yang, Tien-Ju and Chen, Yu-Hsin and Sze, Vivienne, “Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017
Later among the works it cites.
2017
Later among the works it cites.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2017
Later among the works it cites.
E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2017
Later among the works it cites.
K. Zhang, W. Zuo, S. Gu, and L. Zhang, “Learning deep cnn denoiser prior for image restoration,” in IEEE Conference on Computer Vision and Pattern Recognition
2017
Later among the works it cites.
D. Li and Z. Wang, “Video superresolution via motion compensation and deep residual learning,” IEEE Transactions on Computational Imaging
2017
Later among the works it cites.
2017
Later among the works it cites.
GPU Technology Conference
S. Migacz, “8-bit inference with tensorrt,” 2017 · 2017
Later among the works it cites.
2017
Later among the works it cites.
H. Sharma, J. Park, N. Suda, L. Lai, B. Chau, V. Chandra, and H. Esmaeilzadeh, “Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural network,” in ISCA
2018
Closest in time.
E. Park, D. Kim, and S. Yoo, “Energy-efficient neural network accelerator based on outlier-aware low-precision computation,” in ISCA
2018
Closest in time.
S. Sharify, A. D. Lascorz, K. Siu, P. Judd, and A. Moshovos, “Loom: Exploiting weight and activation precisions to accelerate convolutional neural networks,” in Proceedings of the 55th Annual Design Automation Conference
2018
Closest in time.
K. Siu, D. M. Stuart, M. Mahmoud, and A. Moshovos, “Memory requirements for convolutional neural network hardware accelerators,” in IEEE International Symposium on Workload Characterization
2018
Closest in time.
E. Park, S. Yoo, and P. Vajda, “Value-aware quantization for training and inference of neural networks,” in Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part IV
2018
Closest in time.