Title: Evolving Graph Structured Programs for Circuit Generation with Large Language Models
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Electronic Design Automation; Logic Synthesis; Large Language Models;

Abstract: Logic synthesis (LS), which aims to generate a *compact* logic circuit graph with minimized size while *accurately* satisfying a given functionality, plays an important role in chip design. However, existing LS methods struggle to balance circuit structure compactness and functional accuracy, often leading to suboptimal generation. To address this problem, we propose a novel *Circuit Program Evolution* framework, namely CircuitEvo, which iteratively leverages large language models (LLMs) to evolve circuit programs towards improved compactness while preserving functional accuracy. Specifically, CircuitEvo models the circuit graph as a structured program and leverages the strong generative capabilities of LLMs — guided by domain-specific evolutionary prompt strategies — to generate promising circuit candidates in each iteration. Moreover, a structure-aware circuit optimization module is introduced to correct functional discrepancies by appending necessary substructures to the generated circuits.  To the best of our knowledge, CircuitEvo is *the first* LLM-based LS approach that can iteratively improve a circuit's compactness while ensuring functional accuracy.  Experiments on several widely used benchmarks demonstrate that CircuitEvo can efficiently generate accurate circuits with up to 16 input number and 69 output number. Moreover, our method significantly outperforms state-of-the-art methods in terms of circuit size, achieving an average improvement of 6.74%.

TL;DR: A Graph Structured Program Evolution Framework for Circuit Generation with Large Language Models