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A Unified Framework for Automated Code Transformation and Pragma Insertion

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arxiv 2405.03058 v6 pith:DO3S3XO3 submitted 2024-05-05 cs.SE cs.PL

classification cs.SEcs.PL
keywords transformationscodetransformationframeworkinsertionpragmaresultschallenging
verification ladder T0 review T1 audit T2 compute T3 formal
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High-level synthesis, source-to-source compilers, and various Design Space Exploration techniques for pragma insertion have significantly improved the Quality of Results of generated designs. These tools offer benefits such as reduced development time and enhanced performance. However, achieving high-quality results often requires additional manual code transformations and tiling selections, which are typically performed separately or as pre-processing steps. Although DSE techniques enable code transformation upfront, the vastness of the search space often limits the exploration of all possible code transformations, making it challenging to determine which transformations are necessary. Additionally, ensuring correctness remains challenging, especially for complex transformations and optimizations. To tackle this obstacle, we first propose a comprehensive framework leveraging HLS compilers. Our system streamlines code transformation, pragma insertion, and tiles size selection for on-chip data caching through a unified optimization problem, aiming to enhance parallelization, particularly beneficial for computation-bound kernels. Them employing a novel Non-Linear Programming (NLP) approach, we simultaneously ascertain transformations, pragmas, and tile sizes, focusing on regular loop-based kernels. Our evaluation demonstrates that our framework adeptly identifies the appropriate transformations, including scenarios where no transformation is necessary, and inserts pragmas to achieve a favorable Quality of Results.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ForgeHLS: A Large-Scale, Open-Source Dataset for High-Level Synthesis

    cs.AR 2025-07 conditional novelty 6.0 of 10

    ForgeHLS provides 459,850 designs drawn from 846 real-world and GPT-generated kernels to support ML-based high-level synthesis prediction and pragma optimization.

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