DeepRTL2: A Versatile Model for RTL-Related Tasks
This provides a comprehensive solution for hardware designers to accelerate and optimize the design process, though it is incremental as it builds on existing LLM integration.
The paper tackled the gap in electronic design automation by addressing both generation- and embedding-based tasks for register transfer level code, and DeepRTL2 achieved state-of-the-art performance across all evaluated tasks.
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 fine-tuning 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-the-art performance across all evaluated tasks.