An Executable Benchmark for LLM-Based HLS Repair:Design Complexity and Repair Underconstraint

Excerpt

arXiv:2610.03971v1 Announce Type: cross Abstract: Automated repair of High-Level Synthesis (HLS) designs using large language models (LLMs) is an emerging but underexplored problem. While LLM-based repair shows strong results on register-transfer level (RTL) Verilog, the only prior systematic study of HLS logic repair reports just 10.5% correction accuracy for GPT-4, with no analysis of why repair fails or what drives difficulty. We present the first comprehensive evaluation of LLM-based HLS rep