LLM4EDA: Emerging Progress in Large Language Models for Electronic
Design Automation
- URL: http://arxiv.org/abs/2401.12224v1
- Date: Thu, 28 Dec 2023 15:09:14 GMT
- Title: LLM4EDA: Emerging Progress in Large Language Models for Electronic
Design Automation
- Authors: Ruizhe Zhong, Xingbo Du, Shixiong Kai, Zhentao Tang, Siyuan Xu,
Hui-Ling Zhen, Jianye Hao, Qiang Xu, Mingxuan Yuan, Junchi Yan
- Abstract summary: Large Language Models (LLMs) have demonstrated their capability in context understanding, logic reasoning and answer generation.
We present a systematic study on the application of LLMs in the EDA field.
We highlight the future research direction, focusing on applying LLMs in logic synthesis, physical design, multi-modal feature extraction and alignment of circuits.
- Score: 74.7163199054881
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Driven by Moore's Law, the complexity and scale of modern chip design are
increasing rapidly. Electronic Design Automation (EDA) has been widely applied
to address the challenges encountered in the full chip design process. However,
the evolution of very large-scale integrated circuits has made chip design
time-consuming and resource-intensive, requiring substantial prior expert
knowledge. Additionally, intermediate human control activities are crucial for
seeking optimal solutions. In system design stage, circuits are usually
represented with Hardware Description Language (HDL) as a textual format.
Recently, Large Language Models (LLMs) have demonstrated their capability in
context understanding, logic reasoning and answer generation. Since circuit can
be represented with HDL in a textual format, it is reasonable to question
whether LLMs can be leveraged in the EDA field to achieve fully automated chip
design and generate circuits with improved power, performance, and area (PPA).
In this paper, we present a systematic study on the application of LLMs in the
EDA field, categorizing it into the following cases: 1) assistant chatbot, 2)
HDL and script generation, and 3) HDL verification and analysis. Additionally,
we highlight the future research direction, focusing on applying LLMs in logic
synthesis, physical design, multi-modal feature extraction and alignment of
circuits. We collect relevant papers up-to-date in this field via the following
link: https://github.com/Thinklab-SJTU/Awesome-LLM4EDA.
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