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η-LSTM: Co-Designing Highly-Efficient Large LSTM Training via Exploiting Memory-Saving and Architectural Design Opportunities.

X. Zhang, H. Xia, D. Zhuang, H. Sun, X. Fu, M. Taylor, and S. Song. ISCA, page 567-580. IEEE, (2021)

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Guangchao Zhuang

Jinliang Zhuang

Yan Zhuang

Zhifeng Zhuang

Weilun Zhuang

Other publications of authors with the same name

Enabling Highly Efficient Capsule Networks Processing Through Software-Hardware Co-Design.X. Zhang, X. Fu, D. Zhuang, C. Xie, and S. Song. IEEE Trans. Computers, 70 (4): 495-510 (2021)DynamAP: Architectural Support for Dynamic Graph Traversal on the Automata Processor.Y. Liu, X. Zhang, D. Zhuang, X. Fu, and S. Song. ACM Trans. Archit. Code Optim., 19 (4): 60:1-60:26 (2022)Randomness In Neural Network Training: Characterizing The Impact of Tooling.D. Zhuang, X. Zhang, S. Song, and S. Hooker. CoRR, (2021)An Efficient End-to-End Deep Learning Training Framework via Fine-Grained Pattern-Based Pruning.C. Zhang, G. Yuan, W. Niu, J. Tian, S. Jin, D. Zhuang, Z. Jiang, Y. Wang, B. Ren, S. Song and 1 other author(s). CoRR, (2020)Randomness in Neural Network Training: Characterizing the Impact of Tooling.D. Zhuang, X. Zhang, S. Song, and S. Hooker. MLSys, mlsys.org, (2022)Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity.H. Xia, Z. Zheng, Y. Li, D. Zhuang, Z. Zhou, X. Qiu, Y. Li, W. Lin, and S. Song. CoRR, (2023)FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design.H. Xia, Z. Zheng, X. Wu, S. Chen, Z. Yao, S. Youn, A. Bakhtiari, M. Wyatt, D. Zhuang, Z. Zhou and 3 other author(s). CoRR, (2024)ClickTrain: efficient and accurate end-to-end deep learning training via fine-grained architecture-preserving pruning.C. Zhang, G. Yuan, W. Niu, J. Tian, S. Jin, D. Zhuang, Z. Jiang, Y. Wang, B. Ren, S. Song and 1 other author(s). ICS, page 266-278. ACM, (2021)An efficient uncertain graph processing framework for heterogeneous architectures.H. Zhang, L. Li, D. Zhuang, R. Liu, S. Song, D. Tao, Y. Wu, and S. Song. PPoPP, page 477-479. ACM, (2021)η-LSTM: Co-Designing Highly-Efficient Large LSTM Training via Exploiting Memory-Saving and Architectural Design Opportunities.X. Zhang, H. Xia, D. Zhuang, H. Sun, X. Fu, M. Taylor, and S. Song. ISCA, page 567-580. IEEE, (2021)

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Disambiguation of "Zhuang, Donglin"

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η-LSTM: Co-Designing Highly-Efficient Large LSTM Training via Exploiting Memory-Saving and Architectural Design Opportunities.

Please choose a person to relate this publication to

Guangchao Zhuang

Jinliang Zhuang

Yan Zhuang

Zhifeng Zhuang

Weilun Zhuang

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Zhuang, Donglin"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML η-LSTM: Co-Designing Highly-Efficient Large LSTM Training via Exploiting Memory-Saving and Architectural Design Opportunities.

Please choose a person to relate this publication to

Guangchao Zhuang

Jinliang Zhuang

Yan Zhuang

Zhifeng Zhuang

Weilun Zhuang

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

η-LSTM: Co-Designing Highly-Efficient Large LSTM Training via Exploiting Memory-Saving and Architectural Design Opportunities.