Built on

U. Gupta, X. Wang, M. Naumov, C. Wu, B. Reagen, D. Brooks, B. Cottel, K. M. Hazelwood, B. Jia, H. S. Lee, A. Malevich, D. Mudigere, M. Smelyanskiy, L. Xiong, and X. Zhang, “The architectural implications of facebook’s dnn-based personalized recommendation,” CoRR , vol. abs/1906.03109, 2019. [Online]. Available: http://arxiv.org/abs/1906.03109
Original
1906
Earlier work this paper cites.
M. Naumov, D. Mudigere, H. M. Shi, J. Huang, N. Sundaraman, J. Park, X. Wang, U. Gupta, C. Wu, A. G. Azzolini, D. Dzhulgakov, A. Mallevich, I. Cherniavskii, Y. Lu, R. Krishnamoorthi, A. Yu, V. Kondratenko, S. Pereira, X. Chen, W. Chen, V. Rao, B. Jia, L. Xiong, and M. Smelyanskiy, “Deep learning recommendation model for personalization and recommendation systems,” CoRR , vol. abs/1906.00091, 2019. [Online]. Available: http://arxiv.org/abs/1906.00091
Original
1906
Earlier work this paper cites.
A. Tamhane and D. Dunlop, “Statistics and data analysis: from elementary to intermediate,” Prentice Hall , 2000
2000
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting on association for computational linguistics . Association for Computational Linguistics, 2002
2002
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009
2009
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems , 2012
2012
Earlier work this paper cites.
S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, and E. Shelhamer, “cudnn: Efficient primitives for deep learning,” CoRR , vol. abs/1410.0759, 2014. [Online]. Available: http://arxiv.org/abs/1410.0759
Original
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , 2014
2014
Earlier work this paper cites.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional Architecture for Fast Feature Embedding,” in ACM International Conference on Multimedia , 2014
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision . Springer, 2014
2014
Earlier work this paper cites.
T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, and Z. Zhang, “Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems,” arXiv preprint arXiv:1512.01274 , 2015
Original
2015
Earlier work this paper cites.
F. Chollet et al. , “Keras,” https://keras.io , 2015
2015
Earlier work this paper cites.
S. Tokui, K. Oono, S. Hido, and J. Clayton, “Chainer: a next-generation open source framework for deep learning,” in Proceedings of workshop on machine learning systems (LearningSys) in Neural Information Processing Systems (NeurIPS) , vol. 5, 2015
2015
Earlier work this paper cites.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al. , “TensorFlow: A System for Large-Scale Machine Learning,” in OSDI , vol. 16, 2016
2016
Earlier work this paper cites.
R. Adolf, S. Rama, B. Reagen, G.-Y. Wei, and D. Brooks, “Fathom: Reference Workloads for Modern Deep Learning Methods,” in IEEE International Symposium on Workload Characterization (IISWC) , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of Conference on Computer Vision and Pattern Recognition , 2016
2016
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European conference on computer vision . Springer, 2016
2016
Earlier work this paper cites.
F. Seide and A. Agarwal, “Cntk: Microsoft’s open-source deep-learning toolkit,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2016
2016
Earlier work this paper cites.
WMT, “First conference on machine translation,” 2016. [Online]. Available: http://www.statmt.org/wmt16/
2016
Earlier work this paper cites.

Similar

Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey et al. , “Google’s neural machine translation system: Bridging the gap between human and machine translation,” arXiv preprint arXiv:1609.08144 , 2016
Original
2016
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Apple, “Core ml: Integrate machine learning models into your app,” https://developer.apple.com/documentation/coreml , Apple, 2017
2017
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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 , vol. 39, no. 12, 2017
2017
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Baidu, “DeepBench: Benchmarking Deep Learning Operations on Different Hardware,” https://github.com/baidu-research/DeepBench , 2017
2017
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Then

S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection,” The International Journal of Robotics Research , vol. 37, no. 4-5, 2018
2018
Later among the works it cites.
M. Post, “A call for clarity in reporting bleu scores,” arXiv preprint arXiv:1804.08771 , 2018
Original
2018
Later among the works it cites.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of Conference on Computer Vision and Pattern Recognition , 2018
2018
Later among the works it cites.
D. Xu, D. Anguelov, and A. Jain, “Pointfusion: Deep sensor fusion for 3d bounding box estimation,” in Proceedings of Conference on Computer Vision and Pattern Recognition , 2018
2018
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…

Reading the bibliography…

2019

MLPerf Inference Benchmark

Reddi, Vijay Janapa, Cheng, Christine, Kanter, David et al.

Understand

Machine-learning (ML) hardware and software system demand is burgeoning.

Driven by ML applications, the number of different ML inference systems has exploded.
Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and five orders of magnitude in performance; they range from embedded devices to data-center solutions.
Fueling the hardware are a dozen or more software frameworks and libraries.

Built on

U. Gupta, X. Wang, M. Naumov, C. Wu, B. Reagen, D. Brooks, B. Cottel, K. M. Hazelwood, B. Jia, H. S. Lee, A. Malevich, D. Mudigere, M. Smelyanskiy, L. Xiong, and X. Zhang, “The architectural implications of facebook’s dnn-based personalized recommendation,” CoRR , vol. abs/1906.03109, 2019. [Online]. Available: http://arxiv.org/abs/1906.03109
Original
1906
Earlier work this paper cites.
M. Naumov, D. Mudigere, H. M. Shi, J. Huang, N. Sundaraman, J. Park, X. Wang, U. Gupta, C. Wu, A. G. Azzolini, D. Dzhulgakov, A. Mallevich, I. Cherniavskii, Y. Lu, R. Krishnamoorthi, A. Yu, V. Kondratenko, S. Pereira, X. Chen, W. Chen, V. Rao, B. Jia, L. Xiong, and M. Smelyanskiy, “Deep learning recommendation model for personalization and recommendation systems,” CoRR , vol. abs/1906.00091, 2019. [Online]. Available: http://arxiv.org/abs/1906.00091
Original
1906
Earlier work this paper cites.
A. Tamhane and D. Dunlop, “Statistics and data analysis: from elementary to intermediate,” Prentice Hall , 2000
2000
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting on association for computational linguistics . Association for Computational Linguistics, 2002
2002
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009
2009
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems , 2012
2012
Earlier work this paper cites.
S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, and E. Shelhamer, “cudnn: Efficient primitives for deep learning,” CoRR , vol. abs/1410.0759, 2014. [Online]. Available: http://arxiv.org/abs/1410.0759
Original
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , 2014
2014
Earlier work this paper cites.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional Architecture for Fast Feature Embedding,” in ACM International Conference on Multimedia , 2014
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision . Springer, 2014
2014
Earlier work this paper cites.
T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, and Z. Zhang, “Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems,” arXiv preprint arXiv:1512.01274 , 2015
Original
2015
Earlier work this paper cites.
F. Chollet et al. , “Keras,” https://keras.io , 2015
2015
Earlier work this paper cites.
S. Tokui, K. Oono, S. Hido, and J. Clayton, “Chainer: a next-generation open source framework for deep learning,” in Proceedings of workshop on machine learning systems (LearningSys) in Neural Information Processing Systems (NeurIPS) , vol. 5, 2015
2015
Earlier work this paper cites.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al. , “TensorFlow: A System for Large-Scale Machine Learning,” in OSDI , vol. 16, 2016
2016
Earlier work this paper cites.
R. Adolf, S. Rama, B. Reagen, G.-Y. Wei, and D. Brooks, “Fathom: Reference Workloads for Modern Deep Learning Methods,” in IEEE International Symposium on Workload Characterization (IISWC) , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of Conference on Computer Vision and Pattern Recognition , 2016
2016
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European conference on computer vision . Springer, 2016
2016
Earlier work this paper cites.
F. Seide and A. Agarwal, “Cntk: Microsoft’s open-source deep-learning toolkit,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2016
2016
Earlier work this paper cites.
WMT, “First conference on machine translation,” 2016. [Online]. Available: http://www.statmt.org/wmt16/
2016
Earlier work this paper cites.

Similar

Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey et al. , “Google’s neural machine translation system: Bridging the gap between human and machine translation,” arXiv preprint arXiv:1609.08144 , 2016
Original
2016
Cited alongside, same era.
Apple, “Core ml: Integrate machine learning models into your app,” https://developer.apple.com/documentation/coreml , Apple, 2017
2017
Cited alongside, same era.
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 , vol. 39, no. 12, 2017
2017
Cited alongside, same era.
Baidu, “DeepBench: Benchmarking Deep Learning Operations on Different Hardware,” https://github.com/baidu-research/DeepBench , 2017
2017
Cited alongside, same era.

Then

S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection,” The International Journal of Robotics Research , vol. 37, no. 4-5, 2018
2018
Later among the works it cites.
M. Post, “A call for clarity in reporting bleu scores,” arXiv preprint arXiv:1804.08771 , 2018
Original
2018
Later among the works it cites.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of Conference on Computer Vision and Pattern Recognition , 2018
2018
Later among the works it cites.
D. Xu, D. Anguelov, and A. Jain, “Pointfusion: Deep sensor fusion for 3d bounding box estimation,” in Proceedings of Conference on Computer Vision and Pattern Recognition , 2018
2018
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…

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of Conference on Computer Vision and Pattern Recognition , 2017

2017

Cited alongside, same era.

C. Coleman, D. Narayanan, D. Kang, T. Zhao, J. Zhang, L. Nardi, P. Bailis, K. Olukotun, C. Ré, and M. Zaharia, “DAWNBench: An End-to-End Deep Learning Benchmark and Competition,” NeurIPS ML Systems Workshop , 2017

2017

Cited alongside, same era.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861 , 2017

Original

2017

Cited alongside, same era.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017

2017

Cited alongside, same era.

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

Cited alongside, same era.

S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in Proceedings of Conference on Computer Vision and Pattern Recognition , 2017

2017

Cited alongside, same era.

Alibaba, “Ai matrix.” https://aimatrix.ai/en-us/ , Alibaba, 2018

2018

Cited alongside, same era.

D. Amodei and D. Hernandez, “Ai and compute,” https://blog.openai.com/ai-and-compute/ , OpenAI, 2018

2018

Cited alongside, same era.

F. Yu, W. Xian, Y. Chen, F. Liu, M. Liao, V. Madhavan, and T. Darrell, “Bdd100k: A diverse driving video database with scalable annotation tooling,” arXiv preprint arXiv:1805.04687 , 2018

Original

2018

Later among the works it cites.

H. Zhu, M. Akrout, B. Zheng, A. Pelegris, A. Jayarajan, A. Phanishayee, B. Schroeder, and G. Pekhimenko, “Benchmarking and analyzing deep neural network training,” in IEEE International Symposium on Workload Characterization (IISWC) , 2018

2018

Later among the works it cites.

J. Bai, F. Lu, K. Zhang et al. , “Onnx: Open neural network exchange,” https://github.com/onnx/onnx , 2019

2019

Closest in time.

EEMBC, “Introducing the eembc mlmark benchmark,” https://www.eembc.org/mlmark/index.php , Embedded Microprocessor Benchmark Consortium, 2019

2019

Closest in time.

A. Ignatov, R. Timofte, A. Kulik, S. Yang, K. Wang, F. Baum, M. Wu, L. Xu, and L. Van Gool, “Ai benchmark: All about deep learning on smartphones in 2019,” arXiv preprint arXiv:1910.06663 , 2019

Original

2019

Closest in time.

D. Kanter, “Supercomputing 19: Hpc meets machine learning,” https://www.realworldtech.com/sc19-hpc-meets-machine-learning/ , real world technologies, 11 2019

2019

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J. Lee, N. Chirkov, E. Ignasheva, Y. Pisarchyk, M. Shieh, F. Riccardi, R. Sarokin, A. Kulik, and M. Grundmann, “On-device neural net inference with mobile gpus,” arXiv preprint arXiv:1907.01989 , 2019

Original

2019

Closest in time.

K. Lee, V. Rao, and W. C. Arnold, “Accelerating facebook’s infrastructure with application-specific hardware,” https://engineering.fb.com/data-center-engineering/accelerating-infrastructure/ , Facebook, 3 2019

2019

Closest in time.

P. Mattson, C. Cheng, C. Coleman, G. Diamos, P. Micikevicius, D. Patterson, H. Tang, G.-Y. Wei, P. Bailis, V. Bittorf, D. Brooks, D. Chen, D. Dutta, U. Gupta, K. Hazelwood, A. Hock, X. Huang, B. Jia, D. Kang, D. Kanter, N. Kumar, J. Liao, D. Narayanan, T. Oguntebi, G. Pekhimenko, L. Pentecost, V. J. Reddi, T. Robie, T. S. John, C.-J. Wu, L. Xu, C. Young, and M. Zaharia, “Mlperf training benchmark,” arXiv preprint arXiv:1910.01500 , 2019

Original

2019

Closest in time.

MLPerf, “ResNet in TensorFlow,” https://github.com/mlperf/training/tree/master/image_classification/tensorflow/official , 2019

2019

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Principled Technologies, “Aixprt community preview,” https://www.principledtechnologies.com/benchmarkxprt/aixprt/ , 2019

2019

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S. Tang, “Ai-chip,” https://basicmi.github.io/AI-Chip/ , 2019

2019

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P. Wang, X. Huang, X. Cheng, D. Zhou, Q. Geng, and R. Yang, “The apolloscape open dataset for autonomous driving and its application,” IEEE transactions on pattern analysis and machine intelligence , 2019

2019

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P. Mattson, V. J. Reddi, C. Cheng, C. Coleman, G. Diamos, D. Kanter, P. Micikevicius, D. Patterson, G. Schmuelling, H. Tang et al. , “Mlperf: An industry standard benchmark suite for machine learning performance,” IEEE Micro , vol. 40, no. 2, pp. 8–16, 2020

2020

Closest in time.