# C2HLSC: Can LLMs Bridge the Software-to-Hardware Design Gap?

arXiv: https://arxiv.org/abs/2406.09233 · HTML: https://mlsanity.com/paper/2406.09233/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2024.

## Built on by (most cited first, 12 of 12)

- [Automatically Improving LLM-based Verilog Generation using EDA Tool Feedback](https://mlsanity.com/paper/2411.11856/) (2024)
- [HLS-Eval: A Benchmark and Framework for Evaluating LLMs on High-Level Synthesis Design Tasks](https://mlsanity.com/paper/2504.12268/) (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)
- [HLSDebugger: Identification and Correction of Logic Bugs in HLS Code with LLM Solutions](https://mlsanity.com/paper/2507.21485/) (2025)
- [CorrectHDL: Agentic HDL Design with LLMs Leveraging High-Level Synthesis as Reference](https://mlsanity.com/paper/2511.16395/) (2025)
- [Bench4HLS: End-to-End Evaluation of LLMs in High-Level Synthesis Code Generation](https://mlsanity.com/paper/2601.19941/) (2026)
- [AgRefactor: Self-Evolving Agentic Workflow for HLS Compatibility and Performance](https://mlsanity.com/paper/2606.30949/) (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)
- [HLS-Seek: QoR-Aware Code Generation for High-Level Synthesis via Proxy Comparative Reward Reinforcement Learning](https://mlsanity.com/paper/2605.13536/) (2026)
- [Accelerating Post-Quantum Cryptography via LLM-Driven Hardware-Software Co-Design](https://mlsanity.com/paper/2602.09410/) (2026)

## Abstract (arXiv)

High Level Synthesis (HLS) tools offer rapid hardware design from C code, but their compatibility is limited by code constructs. This paper investigates Large Language Models (LLMs) for refactoring C code into HLS-compatible formats. We present several case studies by using an LLM to rewrite C code for NIST 800-22 randomness tests, a QuickSort algorithm and AES-128 into HLS-synthesizable c. The LLM iteratively transforms the C code guided by user prompts, implementing functions like streaming data and hardware-specific signals. This evaluation demonstrates the LLM's potential to assist hardware design refactoring regular C code into HLS synthesizable C code.