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We explore applying the Monte Carlo Tree Search (MCTS) algorithm in a notoriously difficult task: tuning programs for high-performance deep learning and image processing.
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Using machine learning to improve automatic vectorization
Kevin Stock, Louis-Noël Pouchet, and P Sadayappan · 2012
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The effect of compiler optimizations on high-level synthesis for fpgas
Qijing Huang, Ruolong Lian, Andrew Canis, Jongsok Choi, Ryan Xi, Stephen Brown, and Jason Anderson · 2013
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Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines
Jonathan Ragan-Kelley, Connelly Barnes, Andrew Adams, Sylvain Paris, Fredo Durand, and Saman Amarasinghe · 2013
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Opentuner: An extensible framework for program autotuning
Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley, Jeffrey Bosboom, Una-May O’Reilly, and Saman Amarasinghe · 2014
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Tvm: end-to-end optimization stack for deep learning
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Learning to optimize tensor programs
Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy · 2018
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An effective fusion and tile size model for optimizing image processing pipelines
Abhinav Jangda and Uday Bondhugula · 2018
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Ithemal: Accurate, portable and fast basic block throughput estimation using deep neural networks
Charith Mendis, Alex Renda, Saman Amarasinghe, and Michael Carbin · 2018
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Loop transformations leveraging hardware prefetching
Savvas Sioutas, Sander Stuijk, Henk Corporaal, Twan Basten, and Lou Somers · 2018
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Pencil: A platform-neutral compute intermediate language for accelerator programming
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The effect of compiler optimizations on high-level synthesis-generated hardware
Qijing Huang, Ruolong Lian, Andrew Canis, Jongsok Choi, Ryan Xi, Nazanin Calagar, Stephen Brown, and Jason Anderson · 2015
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Polymage: Automatic optimization for image processing pipelines
Ravi Teja Mullapudi, Vinay Vasista, and Uday Bondhugula · 2015
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Throttling automatic vectorization: When less is more
Vasileios Porpodas and Timothy M Jones · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Tensor comprehensions: Framework-agnostic high-performance machine learning abstractions
Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis, Priya Goyal, Zachary DeVito, William S Moses, Sven Verdoolaege, Andrew Adams, and Albert Cohen · 2018
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Machine learning in compiler optimization
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Learning to optimize halide with tree search and random programs
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Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Abdurrahman Akkas, Yunming Zhang, Patricia Suriana, Shoaib Kamil, and Saman Amarasinghe · 2019
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Autophase: Compiler phase-ordering for hls with deep reinforcement learning
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Running and benchmarking halide generators
Steven Johnson · 2019
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Compiler auto-vectorization with imitation learning
Charith Mendis, Cambridge Yang, Yewen Pu, Saman Amarasinghe, and Michael Carbin · 2019
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Schedule synthesis for halide pipelines through reuse analysis
Savvas Sioutas, Sander Stuijk, Luc Waeijen, Twan Basten, Henk Corporaal, and Lou Somers · 2019
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Neurovectorizer: end-to-end vectorization with deep reinforcement learning
Ameer Haj-Ali, Nesreen K Ahmed, Ted Willke, Yakun Sophia Shao, Krste Asanovic, and Ion Stoica · 2020
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Autophase: Juggling hls phase orderings in random forests with deep reinforcement learning
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Mlir: A compiler infrastructure for the end of moore’s law
Chris Lattner, Jacques Pienaar, Mehdi Amini, Uday Bondhugula, River Riddle, Albert Cohen, Tatiana Shpeisman, Andy Davis, Nicolas Vasilache, and Oleksandr Zinenko · 2020
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