LLM4GV: An LLM-Based Flexible Performance-Aware Framework for GEMM Verilog Generation
Dingyang Zou, Gaoche Zhang, Kairui Sun, Zhe Wen, Meiqi Wang, Zhongfeng Wang · 2025
Advancements in AI have increased the demand for specialized AI accelerators, with design for general matrix multiplication (GEMM) module being crucial but time-consuming. While large language models (LLMs) show promise for automating GEMM design, challenges arise from GEMM's vast design space and performance requirements. Existing LLM-based frameworks for RTL code generation often lack flexibility and performance awareness. To overcome the challenges, we propose LLM4GV, a multi-agent LLM-based framework that integrates hardware optimization techniques (HOTs) and performance modeling, improving correctness and performance of the generated code over prior works.