28077

Reinforcement Learning Strategies for Compiler Optimization in High level Synthesis

Hafsah Shahzad, Ahmed Sanaullah, Sanjay Arora, Robert Munafo, Xiteng Yao, Ulrich Drepper, Martin Herbordt
CAAD Lab, ECE Department, Boston University
8th International Workshop on Heterogeneous High Performance Reconfigurable Computing, 2022

@inproceedings{shahzad2022reinforcement,

   title={Reinforcement Learning Strategies for Compiler Optimization in High level Synthesis},

   author={Shahzad, Hafsah and Sanaullah, Ahmed and Arora, Sanjay and Munafo, Robert and Yao, Xiteng and Drepper, Ulrich and Herbordt, Martin},

   booktitle={2022 IEEE/ACM Eighth Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC)},

   pages={13–22},

   year={2022},

   organization={IEEE}

}

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High Level Synthesis (HLS) offers a possible programmability solution for FPGAs by automatically compiling CPU codes to custom hardware configurations, but currently delivers far lower hardware quality than circuits written using Hardware Description Languages (HDLs). One reason is because the standard set of code optimizations used by CPU compilers, such as LLVM, are not well suited for a FPGA back end. Code performance is impacted largely by the order in which passes are applied. Similarly, it is also imperative to find a reasonable number of passes to apply and the optimum pass parameter values. In order to bridge the gap between hand tuned and automatically generated hardware, it is thus important to determine the optimal sequence of passes for HLS compilations, which could vary substantially across different workloads.
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