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AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning

Ameer Haj-Ali, Qijing Huang, William Moses, John Xiang, Ion Stoica, Krste Asanovic, John Wawrzynek
University of California, Berkeley
arXiv:1901.04615 [cs.PL], (15 Jan 2019)

@article{haj2019autophase,

   title={AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning},

   author={Haj-Ali, Ameer and Huang, Qijing and Moses, William and Xiang, John and Stoica, Ion and Asanovic, Krste and Wawrzynek, John},

   journal={arXiv preprint arXiv:1901.04615},

   year={2019}

}

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The performance of the code generated by a compiler depends on the order in which the optimization passes are applied. In the context of high-level synthesis, the quality of the generated circuit relates directly to the code generated by the front-end compiler. Unfortunately, choosing a good order–often referred to as the phase-ordering problem–is an NP-hard problem. As a result, existing solutions rely on a variety of sub-optimal heuristics. In this paper, we evaluate a new technique to address the phase-ordering problem: deep reinforcement learning. To this end, we implement a framework that takes any group of programs and finds a sequence of passes that optimize the performance of these programs. Without loss of generality, we instantiate this framework in the context of an LLVM compiler and target multiple High-Level Synthesis programs. We compare the performance of deep reinforcement learning to state-of-the-art algorithms that address the phase-ordering problem. Overall, our framework runs one to two orders of magnitude faster than these algorithms, and achieves a 16% improvement in circuit performance over the -O3 compiler flag.
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