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AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning
[article]
2019
arXiv
pre-print
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We implement a framework in the context of the LLVM compiler to optimize the ordering for HLS programs and compare the performance of deep reinforcement learning to state-of-the-art algorithms that address ...
In this paper, we evaluate a new technique to address the phase-ordering problem: deep reinforcement learning. ...
Recent advancements in deep reinforcement learning (RL) [1] offer opportunities to address the phase ordering challenge. ...
arXiv:1901.04615v2
fatcat:nga3sq2wqrconhmlm7ndfblsry
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
[article]
2020
arXiv
pre-print
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In this paper, we evaluate a new technique to address the phase-ordering problem: deep reinforcement learning. ...
To this end, we implement AutoPhase: a framework that takes a program and uses deep reinforcement learning to find a sequence of compilation passes that minimizes its execution time. ...
AUTOPHASE FRAMEWORK FOR AUTOMATIC PHASE ORDERING We leverage an existing open-source HLS framework called LegUp that compiles a C program into a hardware RTL design. ...
arXiv:2003.00671v2
fatcat:xemglojhkfhllo7oeo4aosqala
Solving System Problems with Machine Learning
2019
Studies in Informatics and Control
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Over the past decade, Machine Learning (ML) has achieved tremendous successes and has seen wide-scale adoption for human-facing tasks, such as visual recognition, speech recognition, language translation ...
One challenge is that solving many of these problems require solutions that are provably correct, which is at odds with the ML techniques which are stochastic in nature. ...
Supervised learning
Reinforcement Learning With reinforcement learning (RL) [43] , a software agent continuously interacts with the environment by taking actions. ...
doi:10.24846/v28i2y201901
fatcat:fktydwhmzzgvrexswwe246k4pm
Generating GPU Compiler Heuristics using Reinforcement Learning
[article]
2021
arXiv
pre-print
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In this paper, we developed a GPU compiler autotuning framework that uses off-policy deep reinforcement learning to generate heuristics that improve the frame rates of graphics applications. ...
We show that our machine learning-based compiler autotuning framework matches or surpasses the frame rates for 98% of graphics benchmarks with an average uplift of 1.6% up to 15.8%. ...
Acknowledgements We would like to thank Mike Bedy, Robert Gottlieb, Chris Reeve, Andrew Dupont, Karen Dintino, Peter Scannell and the rest of the AMD GPU compiler team for insightful discussions and infrastructure ...
arXiv:2111.12055v1
fatcat:2v3ekxzzfncr5cz6yuunslqspi