# C2HLSC: Leveraging Large Language Models to Bridge the Software-to-Hardware Design Gap

arXiv: https://arxiv.org/abs/2412.00214 · HTML: https://mlsanity.com/paper/2412.00214/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2024.

## Built on by (most cited first, 16 of 16)

- [iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs](https://mlsanity.com/paper/2505.22086/) (2025)
- [LLM Benchmarking with LLaMA2: Evaluating Code Development Performance Across Multiple Programming Languages](https://mlsanity.com/paper/2503.19217/) (2025)
- [Large Language Models (LLMs) for Electronic Design Automation (EDA)](https://mlsanity.com/paper/2508.20030/) (2025)
- [CorrectHDL: Agentic HDL Design with LLMs Leveraging High-Level Synthesis as Reference](https://mlsanity.com/paper/2511.16395/) (2025)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (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)
- [LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges](https://mlsanity.com/paper/2605.10807/) (2026)
- [HLS-Seek: QoR-Aware Code Generation for High-Level Synthesis via Proxy Comparative Reward Reinforcement Learning](https://mlsanity.com/paper/2605.13536/) (2026)
- [Evidence-Driven LLM Agent for C-to-Synthesizable-C Conversion and Verification](https://mlsanity.com/paper/2606.28409/) (2026)
- [SABLE: An NDA-Safe Closed-Loop LLM Framework for Analog Circuit Optimization in Industrial EDA Flows](https://mlsanity.com/paper/2607.03701/) (2026)
- [From Pragmas to Partners: A Symbiotic Evolution of Agentic High-Level Synthesis](https://mlsanity.com/paper/2602.01401/) (2026)
- [A3D: Agentic AI flow for autonomous Accelerator Design](https://mlsanity.com/paper/2605.15237/) (2026)
- [Interpretable and Verifiable Hardware Generation with LLM-Driven Stepwise Refinement](https://mlsanity.com/paper/2606.19387/) (2026)
- [Agentic Electronic Design Automation: A Handoff Perspective](https://mlsanity.com/paper/2606.19795/) (2026)
- [Hardware Design and Security in the Era of Chiplets and LLMs](https://mlsanity.com/paper/2608.05063/) (2026)
- [HLSmith: An Expert-Guided Agentic Framework for C/C++-to-HLS Translation](https://mlsanity.com/paper/2608.06791/) (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 automatically refactoring C code into HLS-compatible formats. We present a case study 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 the system prompt and tool's feedback, implementing functions like streaming data and hardware-specific signals. With the hindsight obtained from the case study, we implement a fully automated framework to refactor C code into HLS-compatible formats using LLMs. To tackle complex designs, we implement a preprocessing step that breaks down the hierarchy in order to approach the problem in a divide-and-conquer bottom-up way. We validated our framework on three ciphers, one hash function, five NIST 800-22 randomness tests, and a QuickSort algorithm. Our results show a high success rate on benchmarks that are orders of magnitude more complex than what has been achieved generating Verilog with LLMs.