@inproceedings{b27880424c0d4a5cafbd0210e928a680,
title = "AutoPhase: Compiler Phase-Ordering for HLS with Deep Reinforcement Learning",
abstract = "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.",
keywords = "Compiler, Deep Reinforcement Learning, HLS, Phase Ordering",
author = "Qijing Huang and Ameer Haj-Ali and William Moses and John Xiang and Ion Stoica and Krste Asanovic and John Wawrzynek",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 27th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2019 ; Conference date: 28-04-2019 Through 01-05-2019",
year = "2019",
month = apr,
doi = "10.1109/FCCM.2019.00049",
language = "English (US)",
series = "Proceedings - 27th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "308",
booktitle = "Proceedings - 27th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2019",
address = "United States",
}