mlsanityData to 7 Oct 2026

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

arXiv 2406.09233 ↗First posted 13 Jun 2024cs.ARcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 4.

12 papers.

  1. 2024Automatically Improving LLM-based Verilog Generation using EDA Tool Feedbackcited by 18
  2. 2025HLS-Eval: A Benchmark and Framework for Evaluating LLMs on High-Level Synthesis Design Taskscited by 10
  3. 2025Can Reasoning Models Reason about Hardware? An Agentic HLS Perspectivecited by 9
  4. 2025LLM-DSE: Searching Accelerator Parameters with LLM Agentscited by 7
  5. 2025HLSDebugger: Identification and Correction of Logic Bugs in HLS Code with LLM Solutionscited by 4
  6. 2025CorrectHDL: Agentic HDL Design with LLMs Leveraging High-Level Synthesis as Referencecited by 3
  7. 2026Bench4HLS: End-to-End Evaluation of LLMs in High-Level Synthesis Code Generationcited by 2
  8. 2026AgRefactor: Self-Evolving Agentic Workflow for HLS Compatibility and Performancecited by 2
  9. 2025Customizing a Large Language Model for VHDL Design of High-Performance Microprocessorscited by 1
  10. 2025Iceberg: Enhancing HLS Modeling with Synthetic Datacited by 1
  11. 2026HLS-Seek: QoR-Aware Code Generation for High-Level Synthesis via Proxy Comparative Reward Reinforcement Learningcited by 1
  12. 2026Accelerating Post-Quantum Cryptography via LLM-Driven Hardware-Software Co-Designcited by 0