Built on

P. Rosenfeld, E. Cooper-Balis, and B. Jacob, “Dramsim2: A cycle accurate memory system simulator,” IEEE Computer Architecture Letters , vol. 10, 2011
2011
Earlier work this paper cites.
G. Chen, C. Parada, and G. Heigold, “Small-footprint keyword spotting using deep neural networks,” in Acoustics, speech and signal processing (icassp), 2014 ieee international conference on . IEEE, 2014
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 , ser. ASPLOS ’14. New York, NY, USA: ACM, 2014. [Online]. Available: http://doi.acm.org.ezp-prod1.hul.harvard.edu/10.1145/2541940.2541967
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 Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture , ser. MICRO-47. Washington, DC, USA: IEEE Computer Society, 2014. [Online]. Available: http://dx.doi.org.ezp-prod1.hul.harvard.edu/10.1109/MICRO.2014.58
2014
Earlier work this paper cites.
A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates et al. , “Deep speech: Scaling up end-to-end speech recognition,” arXiv preprint arXiv:1412.5567 , 2014
Original
2014
Earlier work this paper cites.
R. Johnson and T. Zhang, “Effective use of word order for text categorization with convolutional neural networks,” arXiv preprint arXiv:1412.1058 , 2014
Original
2014
Earlier work this paper cites.
Y. S. Shao, B. Reagen, G.-Y. Wei, and D. Brooks, “Aladdin: A pre-rtl, power-performance accelerator simulator enabling large design space exploration of customized architectures,” in Proceeding of the 41st Annual International Symposium on Computer Architecuture , ser. ISCA ’14. Piscataway, NJ, USA: IEEE Press, 2014. [Online]. Available: http://dl.acm.org/citation.cfm?id=2665671.2665689
2014
Earlier work this paper cites.
N. Chidambaram Nachiappan, P. Yedlapalli, N. Soundararajan, M. T. Kandemir, A. Sivasubramaniam, and C. R. Das, “GemDroid: A Framework to Evaluate Mobile Platforms,” in Proc. of SIGMETRICS , 2014
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,” SIGPLAN Not. , vol. 50, Mar. 2015. [Online]. Available: http://doi.acm.org.ezp-prod1.hul.harvard.edu/10.1145/2775054.2694358
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 Proceedings of the 42Nd Annual International Symposium on Computer Architecture , ser. ISCA ’15. New York, NY, USA: ACM, 2015. [Online]. Available: http://doi.acm.org.ezp-prod1.hul.harvard.edu/10.1145/2749469.2750389
2015
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,” International Journal of Computer Vision , vol. 115, 2015
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems , 2015
2015
Earlier work this paper cites.

Similar

S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan, “Deep learning with limited numerical precision,” in Proceedings of The 32nd International Conference on Machine Learning , 2015
2015
Cited alongside, same era.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016
2016
Cited alongside, same era.
L. Tai and M. Liu, “Deep-learning in mobile robotics-from perception to control systems: A survey on why and why not,” arXiv preprint arXiv:1612.07139 , 2016
Original
2016
Cited alongside, same era.
Y.-H. Chen, T. Krishna, J. Emer, and V. Sze, “Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks,” in International Solid-State Circuits Conference , ser. ISSCC, 2016
2016
Cited alongside, same era.

Then

A. Parashar, M. Rhu, A. Mukkara, A. Puglielli, R. Venkatesan, B. Khailany, J. S. Emer, S. W. Keckler, and W. J. Dally, “SCNN: an accelerator for compressed-sparse convolutional neural networks,” CoRR , vol. abs/1708.04485, 2017. [Online]. Available: http://arxiv.org/abs/1708.04485
Original
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. Cantin, C. Chao, C. Clark, J. Coriell, M. Daley, M. Dau, J. Dean, B. Gelb, T. V. Ghaemmaghami, R. Gottipati, W. Gulland, R. Hagmann, R. C. Ho, D. Hogberg, J. Hu, R. Hundt, D. Hurt, J. Ibarz, A. Jaffey, A. Jaworski, A. Kaplan, H. Khaitan, 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, 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,” CoRR , vol. abs/1704.04760, 2017. [Online]. Available: http://arxiv.org/abs/1704.04760
Original
2017
Later among the works it cites.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton et al. , “Mastering the game of go without human knowledge,” Nature , vol. 550, 2017
2017
Later among the works it cites.

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Reading the bibliography…

2018

SCALE-Sim: Systolic CNN Accelerator Simulator

Samajdar, Ananda, Zhu, Yuhao, Whatmough, Paul et al.

Understand

Systolic Arrays are one of the most popular compute substrates within Deep Learning accelerators today, as they provide extremely high efficiency for running dense matrix multiplications.

However, the research community lacks tools to insights on both the design trade-offs and efficient mapping strategies for systolic-array based accelerators.
We introduce Systolic CNN Accelerator Simulator (SCALE-Sim), which is a configurable systolic array based cycle accurate DNN accelerator simulator.
SCALE-Sim exposes various micro-architectural features as well as system integration parameters to the designer to enable comprehensive design space exploration.

Built on

P. Rosenfeld, E. Cooper-Balis, and B. Jacob, “Dramsim2: A cycle accurate memory system simulator,” IEEE Computer Architecture Letters , vol. 10, 2011
2011
Earlier work this paper cites.
G. Chen, C. Parada, and G. Heigold, “Small-footprint keyword spotting using deep neural networks,” in Acoustics, speech and signal processing (icassp), 2014 ieee international conference on . IEEE, 2014
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 , ser. ASPLOS ’14. New York, NY, USA: ACM, 2014. [Online]. Available: http://doi.acm.org.ezp-prod1.hul.harvard.edu/10.1145/2541940.2541967
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 Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture , ser. MICRO-47. Washington, DC, USA: IEEE Computer Society, 2014. [Online]. Available: http://dx.doi.org.ezp-prod1.hul.harvard.edu/10.1109/MICRO.2014.58
2014
Earlier work this paper cites.
A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates et al. , “Deep speech: Scaling up end-to-end speech recognition,” arXiv preprint arXiv:1412.5567 , 2014
Original
2014
Earlier work this paper cites.
R. Johnson and T. Zhang, “Effective use of word order for text categorization with convolutional neural networks,” arXiv preprint arXiv:1412.1058 , 2014
Original
2014
Earlier work this paper cites.
Y. S. Shao, B. Reagen, G.-Y. Wei, and D. Brooks, “Aladdin: A pre-rtl, power-performance accelerator simulator enabling large design space exploration of customized architectures,” in Proceeding of the 41st Annual International Symposium on Computer Architecuture , ser. ISCA ’14. Piscataway, NJ, USA: IEEE Press, 2014. [Online]. Available: http://dl.acm.org/citation.cfm?id=2665671.2665689
2014
Earlier work this paper cites.
N. Chidambaram Nachiappan, P. Yedlapalli, N. Soundararajan, M. T. Kandemir, A. Sivasubramaniam, and C. R. Das, “GemDroid: A Framework to Evaluate Mobile Platforms,” in Proc. of SIGMETRICS , 2014
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,” SIGPLAN Not. , vol. 50, Mar. 2015. [Online]. Available: http://doi.acm.org.ezp-prod1.hul.harvard.edu/10.1145/2775054.2694358
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 Proceedings of the 42Nd Annual International Symposium on Computer Architecture , ser. ISCA ’15. New York, NY, USA: ACM, 2015. [Online]. Available: http://doi.acm.org.ezp-prod1.hul.harvard.edu/10.1145/2749469.2750389
2015
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,” International Journal of Computer Vision , vol. 115, 2015
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems , 2015
2015
Earlier work this paper cites.

Similar

S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan, “Deep learning with limited numerical precision,” in Proceedings of The 32nd International Conference on Machine Learning , 2015
2015
Cited alongside, same era.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016
2016
Cited alongside, same era.
L. Tai and M. Liu, “Deep-learning in mobile robotics-from perception to control systems: A survey on why and why not,” arXiv preprint arXiv:1612.07139 , 2016
Original
2016
Cited alongside, same era.
Y.-H. Chen, T. Krishna, J. Emer, and V. Sze, “Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks,” in International Solid-State Circuits Conference , ser. ISSCC, 2016
2016
Cited alongside, same era.

Then

A. Parashar, M. Rhu, A. Mukkara, A. Puglielli, R. Venkatesan, B. Khailany, J. S. Emer, S. W. Keckler, and W. J. Dally, “SCNN: an accelerator for compressed-sparse convolutional neural networks,” CoRR , vol. abs/1708.04485, 2017. [Online]. Available: http://arxiv.org/abs/1708.04485
Original
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. Cantin, C. Chao, C. Clark, J. Coriell, M. Daley, M. Dau, J. Dean, B. Gelb, T. V. Ghaemmaghami, R. Gottipati, W. Gulland, R. Hagmann, R. C. Ho, D. Hogberg, J. Hu, R. Hundt, D. Hurt, J. Ibarz, A. Jaffey, A. Jaworski, A. Kaplan, H. Khaitan, 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, 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,” CoRR , vol. abs/1704.04760, 2017. [Online]. Available: http://arxiv.org/abs/1704.04760
Original
2017
Later among the works it cites.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton et al. , “Mastering the game of go without human knowledge,” Nature , vol. 550, 2017
2017
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Y. S. Shao, S. L. Xi, V. Srinivasan, G.-Y. Wei, and D. Brooks, “Co-designing accelerators and soc interfaces using gem5-aladdin,” in Microarchitecture (MICRO), 2016 49th Annual IEEE/ACM International Symposium on . IEEE, 2016

2016

Cited alongside, same era.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016

2016

Cited alongside, same era.

D. Amodei, S. Ananthanarayanan, R. Anubhai, J. Bai, E. Battenberg, C. Case, J. Casper, B. Catanzaro, Q. Cheng, G. Chen et al. , “Deep speech 2: End-to-end speech recognition in english and mandarin,” in International Conference on Machine Learning , 2016

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 , ser. ISCA, 2016

2016

Cited alongside, same era.

P. Judd, J. Albericio, T. Hetherington, T. M. Aamodt, and A. Moshovos, “Stripes: Bit-serial deep neural network computing,” in 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) , Oct 2016

2016

Cited alongside, same era.

S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. Horowitz, and W. Dally, “EIE: Efficient inference engine on compressed deep neural network,” in Proceedings of the 43rd International Symposium on Computer Architecture , ser. ISCA, 2016

2016

Cited alongside, same era.

T.-J. Yang, Y.-H. Chen, and V. Sze, “Designing energy-efficient convolutional neural networks using energy-aware pruning,” arXiv preprint arXiv:1611.05128 , 2016

Original

2016

Cited alongside, same era.

M. Halpern, Y. Zhu, and V. J. Reddi, “Mobile CPU’s Rise to Power: Quantifying the Impact of Generational Mobile CPU Design Trends on Performance, Energy, and User Satisfaction,” in Proc. of HPCA , 2016

2016

Cited alongside, same era.

X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative filtering,” in Proceedings of the 26th International Conference on World Wide Web . International World Wide Web Conferences Steering Committee, 2017

2017

Later among the works it cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , 2017

2017

Later among the works it cites.

W. Lu, G. Yan, J. Li, S. Gong, Y. Han, and X. Li, “Flexflow: A flexible dataflow accelerator architecture for convolutional neural networks,” in High Performance Computer Architecture (HPCA), 2017 IEEE International Symposium on . IEEE, 2017

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,” ACM SIGOPS Operating Systems Review , vol. 51, 2017

2017

Later among the works it cites.

“Xilinx ml suite,” https://github.com/Xilinx/ml-suite

2018

Closest in time.

“Mlperf: Mlperf benchmark suite,” https://github.com/mlperf

2018

Closest in time.

H. Kwon, A. Samajdar, and T. Krishna, “Maeri: Enabling flexible dataflow mapping over dnn accelerators via reconfigurable interconnects,” in Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems . ACM, 2018

2018

Closest in time.

Y. Zhu, A. Samajdar, M. Mattina, and P. Whatmough, “Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision,” in Proc. of ISCA , 2018

2018

Closest in time.