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

• URL: http://arxiv.org/abs/2412.00214v1
• Date: Fri, 29 Nov 2024 19:22:52 GMT
• Title: C2HLSC: Leveraging Large Language Models to Bridge the Software-to-Hardware Design Gap
• Authors: Luca Collini, Siddharth Garg, Ramesh Karri,
• Abstract summary: This paper investigates Large Language Models for automatically C code into HLS-compatible formats.<n>We present a case study using an LLM to rewrite C code for NIST 800-22 tests randomness, a QuickSort algorithm, and AES-128 into HLS-synthesizable C.
• Score: 20.528640971583112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, 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.

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