# Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models

arXiv: https://arxiv.org/abs/2407.03889 · HTML: https://mlsanity.com/paper/2407.03889/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2024.

## Built on by (most cited first, 18 of 18)

- [Agents4PLC: Automating Closed-loop PLC Code Generation and Verification in Industrial Control Systems using LLM-based Agents](https://mlsanity.com/paper/2410.14209/) (2024)
- [VeriMoA: A Mixture-of-Agents Framework for Spec-to-HDL Generation](https://mlsanity.com/paper/2510.27617/) (2025)
- [LLM-DSE: Searching Accelerator Parameters with LLM Agents](https://mlsanity.com/paper/2505.12188/) (2025)
- [Can Reasoning Models Reason about Hardware? An Agentic HLS Perspective](https://mlsanity.com/paper/2503.12721/) (2025)
- [LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning](https://mlsanity.com/paper/2504.21187/) (2025)
- [iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs](https://mlsanity.com/paper/2505.22086/) (2025)
- [Large Language Models (LLMs) for Electronic Design Automation (EDA)](https://mlsanity.com/paper/2508.20030/) (2025)
- [CktEvo: Repository-Level RTL Code Benchmark for Design Evolution](https://mlsanity.com/paper/2603.08718/) (2026)
- [Bench4HLS: End-to-End Evaluation of LLMs in High-Level Synthesis Code Generation](https://mlsanity.com/paper/2601.19941/) (2026)
- [Customizing a Large Language Model for VHDL Design of High-Performance Microprocessors](https://mlsanity.com/paper/2505.09610/) (2025)
- [Iceberg: Enhancing HLS Modeling with Synthetic Data](https://mlsanity.com/paper/2507.09948/) (2025)
- [Agent Factories for High Level Synthesis: How Far Can General-Purpose Coding Agents Go in Hardware Optimization?](https://mlsanity.com/paper/2603.25719/) (2026)
- [Evidence-Driven LLM Agent for C-to-Synthesizable-C Conversion and Verification](https://mlsanity.com/paper/2606.28409/) (2026)
- [From Pragmas to Partners: A Symbiotic Evolution of Agentic High-Level Synthesis](https://mlsanity.com/paper/2602.01401/) (2026)
- [TOPCELL: Topology Optimization of Standard Cell via LLMs](https://mlsanity.com/paper/2604.14237/) (2026)
- [A3D: Agentic AI flow for autonomous Accelerator Design](https://mlsanity.com/paper/2605.15237/) (2026)
- [Agentic Electronic Design Automation: A Handoff Perspective](https://mlsanity.com/paper/2606.19795/) (2026)
- [LLM for EDA in Front-End Design: Challenges and Opportunities](https://mlsanity.com/paper/2607.09616/) (2026)

## Abstract (arXiv)

In High-Level Synthesis (HLS), converting a regular C/C++ program into its HLS-compatible counterpart (HLS-C) still requires tremendous manual effort. Various program scripts have been introduced to automate this process. But the resulting codes usually contain many issues that should be manually repaired by developers. Since Large Language Models (LLMs) have the ability to automate code generation, they can also be used for automated program repair in HLS. However, due to the limited training of LLMs considering hardware and software simultaneously, hallucinations may occur during program repair using LLMs, leading to compilation failures. Besides, using LLMs for iterative repair also incurs a high cost. To address these challenges, we propose an LLM-driven program repair framework that takes regular C/C++ code as input and automatically generates its corresponding HLS-C code for synthesis while minimizing human repair effort. To mitigate the hallucinations in LLMs and enhance the prompt quality, a Retrieval-Augmented Generation (RAG) paradigm is introduced to guide the LLMs toward correct repair. In addition, we use LLMs to create a static bit width optimization program to identify the optimized bit widths for variables. Moreover, LLM-driven HLS optimization strategies are introduced to add/tune pragmas in HLS-C programs for circuit optimization. Experimental results demonstrate that the proposed LLM-driven automated framework can achieve much higher repair pass rates in 24 real-world applications compared with the traditional scripts and the direct application of LLMs for program repair.