Optimizing Deep Learning Inference via Global Analysis and Tensor Expressions-Reference-Cited by-同舟云学术

Optimizing Deep Learning Inference via Global Analysis and Tensor Expressions

Published:2024-04-17 Issue: Volume:2 Page:286-301
ISSN:
Container-title:Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1
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Author:

Xia Chunwei¹²³^ORCID,Zhao Jiacheng¹²^ORCID,Sun Qianqi¹²^ORCID,Wang Zheng³^ORCID,Wen Yuan⁴^ORCID,Yu Teng⁵^ORCID,Feng Xiaobing¹²⁶^ORCID,Cui Huimin¹²^ORCID

Affiliation:

1. State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

2. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

3. School of Computing, University of Leeds, Leeds, United Kingdom

4. University of Aberdeen, Aberdeen, United Kingdom

5. Thewake Systems Ltd, Beijing, China

6. Zhongguancun Laboratory, Beijing, China

Funder

National Key R&D Program of China

the National Natural Science Foundation of China

the Innovation Funding of ICT CAS

Beijing Nova Program

the UK Engineering and Physical Sciences Research Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3617232.3624858

Reference60 articles.

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2. Martín Abadi, Paul Barham, Jianmin Chen, et al. 2016. TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, 265--283. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi

3. Polyhedral auto-transformation with no integer linear programming

4. On optimizing machine learning workloads via kernel fusion

5. Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code