HLSRewriter: Efficient Refactoring and Optimization of C/C++ Code with LLMs for High-Level Synthesis
Bibliographic record
Abstract
In High-Level Synthesis (HLS), refactoring a standard C/C++ code into its HLS-compatible version (HLS-C) still requires significant human effort. While various program scripts have been introduced to automate this process, the resulting code still contains many HLS-incompatible issues that need to be manually refactored and optimized by developers. Since Large Language Models (LLMs) have the ability to automate code generation, they can also be used for automated code refactoring and optimization in HLS. However, due to the limited training of LLMs, considering hardware and software simultaneously, hallucinations may occur when using LLMs for HLS, leading to synthesis failures. To address these challenges, we introduce HLSRewriter , an LLM-aided code refactoring and optimization framework that takes regular C/C++ code as input and automatically generates its corresponding optimized HLS-C code for hardware synthesis with minimal human intervention. To mitigate LLM hallucinations, a step-wise reasoning process is employed to analyze and detect HLS-incompatible errors. Afterwards, a repair library containing reference templates is efficiently created by scanning the HLS tool manual, followed by cooperation with a Retrieval-Augmented Generation (RAG) paradigm to guide the LLMs toward correct refactoring. In addition, a pipeline-aware decomposition strategy is introduced to progressively break down complex loop structures into smaller tasks with a balanced trade-off between latency and area, thereby enabling efficient pipelining and parallel execution. To further improve hardware efficiency, a bit width adjuster module is incorporated into this framework to optimize the precision of floating-point variables. Moreover, LLM-aided HLS optimization strategies are introduced to add/tune hardware directives in HLS-C code, thereby enhancing the performance of the final synthesized hardware. Experimental results demonstrate that the proposed LLM-aided framework can achieve higher refactoring pass rates and superior hardware performance in 24 real-world tasks compared with traditional approaches and the direct application of LLMs for code refactoring and optimization. The codes are open-sourced at this link: https://github.com/code-source1/catapult .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".