Year: 2025
Type: conference-paper
Abstract: The integration of large language models (LLMs) into electronic design automation (EDA) has significantly advanced the field, offering transformative benefits, particularly in register transfer level (RTL) code generation and understanding.While previous studies have demonstrated the efficacy of finetuning LLMs for these generation-based tasks, embedding-based tasks, which are equally critical to EDA workflows, have been largely overlooked.These tasks, including natural language code search, RTL code functionality equivalence checking, and performance prediction, are essential for accelerating and optimizing the hardware design process.To address this gap, we present DeepRTL2, a family of versatile LLMs that unifies both generation-and embedding-based tasks related to RTL.By simultaneously tackling a broad range of tasks, DeepRTL2 represents the first model to provide a comprehensive solution to the diverse challenges in EDA.Through extensive experiments, we show that DeepRTL2 achieves state-of-theart performance across all evaluated tasks.
Authors: Yi Liu, Hongji Zhang, Yunhao Zhou, Zhengyuan Shi, Changran Xu, Qiang Xu
Institution: Chinese University of Hong Kong
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412888608