The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
A domain-specific supercomputer for training deep neural networks
Open accessAbstract
Google's TPU supercomputers train deep neural networks 50x faster than general-purpose supercomputers running a high-performance computing benchmark.
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 14,214
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Computer Architecture: A Quantitative Approach
Hennessy, J. L., & Patterson, D. A. (1989) · UCLA - Biblioteca de Ciencias y Tecnologia Felix Morales Bueno · Cited by 9,370
Self-citation(2010) · Cited by 8,261
Neural Architecture Search with Reinforcement Learning
Zoph, B., & Le, Q. V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01578 · Cited by 3,974
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562 · cited 1×
“CNN0 is AlphaZero, a reinforcement learning algorithm with extensive use of CNNs, which mastered the games chess, Go, and shogi.(34) CNN1 is a Google-internal Figure 6.”
Large Scale Distributed Deep Networks
Dean, J. B., Corrado, G. S., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P. A., Yang, K., et al. (2012) · Cited by 3,199
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PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590
“Hardware & Software: Training Hardware: TPU v4 (Jouppi et al., 2020).”
BackgroundMethodInfluentialGemini: A Family of Highly Capable Multimodal Models
Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv · doi:10.48550/arxiv.2312.11805 · Cited by 990
Self-citationThe Computational Limits of Deep Learning
Thompson, N. C., Greenewald, K., Lee, K., & Manso, G. F. (2020) · arXiv · doi:10.48550/arxiv.2007.05558 · Cited by 538
Jouppi, N., Kurian, G., Li, S., Ma, P., Nagarajan, R., Nai, L., Patil, N., Subramanian, S., Swing, A., Towles, B., et al. (2023) · Proceedings of the 50th Annual International Symposium on Computer Architecture · doi:10.1145/3579371.3589350 · Cited by 508
Self-citationSingle chip photonic deep neural network with accelerated training
Bandyopadhyay, S., Sludds, A., Krastanov, S., Hamerly, R., Harris, N., Bunandar, D., Streshinsky, M., Hochberg, M., & Englund, D. (2022) · arXiv · doi:10.48550/arxiv.2208.01623 · Cited by 265
Metadata
- Authors
- Jouppi, N. P., Yoon, D. H., Kurian, G., Li, S., Patil, N., Laudon, J., Young, C., & Patterson, D.
- Venue
- Communications of the ACM
- Published
- 2020-06-18
- Volume
- 63
- Issue
- 7
- Pages
- 67-78
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W3036878841
- Cited by
- 258
- References
- 29
- FWCI
- 30.73
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Jouppi, N. P., Yoon, D. H., Kurian, G., Li, S., Patil, N., Laudon, J., Young, C., & Patterson, D. (2020). A domain-specific supercomputer for training deep neural networks. Communications of the ACM. https://doi.org/10.1145/3360307
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
