SpecLLM: Exploring Generation and Review of VLSI Design Specification with Large Language Model
Mengming Li, Wenji Fang, Qijun Zhang, Zhiyao Xie

TL;DR
This paper investigates using large language models to automate the creation and review of VLSI architecture specifications, aiming to improve efficiency and reduce errors in the chip design process.
Contribution
It introduces a novel application of LLMs for generating and reviewing VLSI specifications, addressing a critical gap in electronic design automation.
Findings
LLMs can generate detailed architecture specifications from scratch.
LLMs can convert RTL code into comprehensive specifications.
Promising results suggest potential for LLMs to enhance IC design workflows.
Abstract
The development of architecture specifications is an initial and fundamental stage of the integrated circuit (IC) design process. Traditionally, architecture specifications are crafted by experienced chip architects, a process that is not only time-consuming but also error-prone. Mistakes in these specifications may significantly affect subsequent stages of chip design. Despite the presence of advanced electronic design automation (EDA) tools, effective solutions to these specification-related challenges remain scarce. Since writing architecture specifications is naturally a natural language processing (NLP) task, this paper pioneers the automation of architecture specification development with the advanced capabilities of large language models (LLMs). Leveraging our definition and dataset, we explore the application of LLMs in two key aspects of architecture specification development:…
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Taxonomy
TopicsMachine Learning in Materials Science · Semiconductor materials and devices · Advancements in Photolithography Techniques
