Automating reinforcement learning architecture design for code optimization-Reference-Cited by-同舟云学术

Automating reinforcement learning architecture design for code optimization

Published:2022-03-18 Issue: Volume: Page:
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Container-title:Proceedings of the 31st ACM SIGPLAN International Conference on Compiler Construction
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Author:

Wang Huanting¹,Tang Zhanyong²,Zhang Cheng²,Zhao Jiaqi²,Cummins Chris³,Leather Hugh³^ORCID,Wang Zheng⁴^ORCID

Affiliation:

1. NorthWest University, China / University of Leeds, UK

2. NorthWest University, China

3. Meta AI, USA

4. University of Leeds, UK

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3497776.3517769

Reference93 articles.

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2. [n. d.]. llvm-test-suite. https://github.com/llvm/llvm-test-suite [n. d.]. llvm-test-suite. https://github.com/llvm/llvm-test-suite

3. Learning to optimize halide with tree search and random programs

4. Using Machine Learning to Focus Iterative Optimization

5. Byung Hoon Ahn , Prannoy Pilligundla , Amir Yazdanbakhsh , and Hadi Esmaeilzadeh . 2019 . Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. In International Conference on Learning Representations. ICLR’20 . Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, and Hadi Esmaeilzadeh. 2019. Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. In International Conference on Learning Representations. ICLR’20.

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