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ROLLER: Fast and Efficient Tensor Compilation for Deep Learning.

H. Zhu, R. Wu, Y. Diao, S. Ke, H. Li, C. Zhang, J. Xue, L. Ma, Y. Xia, W. Cui, F. Yang, M. Yang, L. Zhou, A. Cidon, and G. Pekhimenko. OSDI, page 233-248. USENIX Association, (2022)

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Lingxiao Wang

Lingxiao Zhou

Yuzhao Ma

Characterization of higher order modes in optical fibersY. Ma. Uni Erlangen-Nürnberg, (2009)

Lan Ma

Jingxiang Ma

Other publications of authors with the same name

Welder: Scheduling Deep Learning Memory Access via Tile-graph.Y. Shi, Z. Yang, J. Xue, L. Ma, Y. Xia, Z. Miao, Y. Guo, F. Yang, and L. Zhou. OSDI, page 701-718. USENIX Association, (2023)Optimizing Dynamic Neural Networks with Brainstorm.W. Cui, Z. Han, L. Ouyang, Y. Wang, N. Zheng, L. Ma, Y. Yang, F. Yang, J. Xue, L. Qiu and 4 other author(s). OSDI, page 797-815. USENIX Association, (2023)SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization.S. Cao, L. Ma, W. Xiao, C. Zhang, Y. Liu, L. Zhang, L. Nie, and Z. Yang. CVPR, page 11216-11225. Computer Vision Foundation / IEEE, (2019)LUT Tensor Core: Lookup Table Enables Efficient Low-Bit LLM Inference Acceleration.Z. Mo, L. Wang, J. Wei, Z. Zeng, S. Cao, L. Ma, N. Jing, T. Cao, J. Xue, F. Yang and 1 other author(s). CoRR, (2024)SparTA: Deep-Learning Model Sparsity via Tensor-with-Sparsity-Attribute.N. Zheng, B. Lin, Q. Zhang, L. Ma, Y. Yang, F. Yang, Y. Wang, M. Yang, and L. Zhou. OSDI, page 213-232. USENIX Association, (2022)NeuGraph: Parallel Deep Neural Network Computation on Large Graphs.L. Ma, Z. Yang, Y. Miao, J. Xue, M. Wu, L. Zhou, and Y. Dai. USENIX ATC, page 443-458. USENIX Association, (2019)CuWide: Towards Efficient Flow-based Training for Sparse Wide Models on GPUs (Extended Abstract).X. Miao, L. Ma, Z. Yang, Y. Shao, B. Cui, L. Yu, and J. Jiang. ICDE, page 2330-2331. IEEE, (2021)Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce.X. Miao, X. Nie, Y. Shao, Z. Yang, J. Jiang, L. Ma, and B. Cui. SIGMOD Conference, page 2262-2270. ACM, (2021)The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.S. Ma, H. Wang, L. Ma, L. Wang, W. Wang, S. Huang, L. Dong, R. Wang, J. Xue, and F. Wei. CoRR, (2024)PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network.C. Tian, L. Ma, Z. Yang, and Y. Dai. IPDPS, page 936-945. IEEE, (2020)

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Disambiguation of "Ma, Lingxiao"

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ROLLER: Fast and Efficient Tensor Compilation for Deep Learning.

Please choose a person to relate this publication to

Lingxiao Wang

Lingxiao Zhou

Yuzhao Ma

Lan Ma

Jingxiang Ma

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Ma, Lingxiao"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML ROLLER: Fast and Efficient Tensor Compilation for Deep Learning.

Please choose a person to relate this publication to

Lingxiao Wang

Lingxiao Zhou

Yuzhao Ma

Lan Ma

Jingxiang Ma

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
BibTeX
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APA
Chicago
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ROLLER: Fast and Efficient Tensor Compilation for Deep Learning.