A Unified Framework for Automated Code Transformation and Pragma Insertion
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 May 2024.
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Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2025iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMscited by 7
- 2026AgRefactor: Self-Evolving Agentic Workflow for HLS Compatibility and Performancecited by 2
- 2025Iceberg: Enhancing HLS Modeling with Synthetic Datacited by 1
- 2026Report for NSF Workshop on AI for Electronic Design Automationcited by 1
- 2026HLSmith: An Expert-Guided Agentic Framework for C/C++-to-HLS Translationcited by 0
- 2026HLSmith: An Expert-Guided Agentic Framework for C/C++-to-HLS Translation7 Aug 2026
- 2026AgRefactor: Self-Evolving Agentic Workflow for HLS Compatibility and Performance29 Jun 2026
- 2026Report for NSF Workshop on AI for Electronic Design Automation20 Jan 2026
- 2025Iceberg: Enhancing HLS Modeling with Synthetic Data14 Jul 2025
- 2025iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs28 May 2025