AutoPhase: Compiler Phase-Ordering for HLS with Deep Reinforcement Learning
AutoPhase: Compiler Phase-Ordering for HLS with Deep Reinforcement Learning
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DOI：
10.1109/fccm.2019.00049
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发表时间：
2019-01
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影响因子：
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通讯作者：
Qijing Huang;Ameer Haj-Ali;William S. Moses;J. Xiang;I. Stoica;K. Asanović;J. Wawrzynek
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文献类型：
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作者：
Qijing Huang;Ameer Haj-Ali;William S. Moses;J. Xiang;I. Stoica;K. Asanović;J. Wawrzynek
编译器生成的代码的性能取决于应用优化通道的顺序。在高级综合中，生成电路的质量与前端编译器生成的代码直接相关。选择一个好的顺序--通常被称为相序问题--是一个NP困难的问题。在本文中，我们评估了一种解决相序问题的新技术：深度强化学习。我们在LLVM编译器的环境下实现了一个框架来优化HLS程序的排序，并将深度强化学习的性能与解决阶段排序问题的最新算法进行了比较。总体而言，我们的框架运行速度比这些算法快一到两个数量级，并且电路性能比-03编译器标志提高了16%。
The performance of the code generated by a compiler depends on the order in which the optimization passes are applied. In high-level synthesis, the quality of the generated circuit relates directly to the code generated by the front-end compiler. Choosing a good order–often referred to as the phase-ordering problem–is an NP-hard problem. In this paper, we evaluate a new technique to address the phase-ordering problem: deep reinforcement learning. 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 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.