“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 Wawrzynek2019-01-15 (algorithms, model-free RL; backlinks; similar)⁠:

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.

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