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We show that, during inference with Convolutional Neural Networks (CNNs), more than 2x to $8x ineffectual work can be exposed if instead of targeting those weights and activations that are zero, we target different combinations of value stream properties.
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.
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.
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.
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.
S. Han, H. Mao, and W. J. Dally, “Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding,” arXiv:1510.00149 [cs] · 2015
Earlier work this paper cites.
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
2015
Earlier work this paper cites.
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
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 Proceedings of the 43rd International Symposium on Computer Architecture
2016
Earlier work this paper cites.
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
Earlier work this paper cites.
J. Albericio, P. Judd, T. Hetherington, T. Aamodt, N. Enright 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.
P. Warden, “Low-precision matrix multiplication.” https://petewarden.com , 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
P. Judd, J. Albericio, T. Hetherington, T. Aamodt, and A. Moshovos, “Stripes: Bit-serial Deep Neural Network Computing ,” in Proceedings of the 49th Annual IEEE/ACM International Symposium on Microarchitecture
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 2016 IEEE/ACM International Conference on Computer Architecture (ISCA)
2016
Later among the works it cites.
P. Judd, J. Albericio, T. Hetherington, T. M. Aamodt, N. E. Jerger, and A. Moshovos, “Proteus: Exploiting numerical precision variability in deep neural networks,” in Proceedings of the 2016 International Conference on Supercomputing
2016
Later among the works it cites.
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 Proceedings of the 44th Annual International Symposium on Computer Architecture
2017
Later among the works it cites.
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
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2016
Cited alongside, same era.
Y.-H. Chen, J. Emer, and V. Sze, “Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks,” in Proceedings of the 43rd International Symposium on Computer Architecture
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 Proceedings of the 43rd International Symposium on Computer Architecture
2016
Cited alongside, same era.
Y. Guo, A. Yao, and Y. Chen, “Dynamic network surgery for efficient dnns,” CoRR
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 49th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2016, Taipei, Taiwan, October 15-19, 2016
2016
Cited alongside, same era.
N. Muralimanohar and R. Balasubramonian, “Cacti 6.0: A tool to understand large caches.”
Cited in the paper.
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.
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.
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.
2017
Later among the works it cites.
M. Gao, J. Pu, X. Yang, M. Horowitz, and C. Kozyrakis, “Tetris: Scalable and efficient neural network acceleration with 3d memory,” in Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems
2017
Later among the works it cites.