AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies

2025-02-28

AI Summary

Analog IC topology design remains heavily reliant on manual expertise, resulting in low efficiency and limited creativity. Method: This paper introduces the first generative AI framework for analog circuit topology synthesis. We construct the first large-scale analog circuit topology dataset, propose a unified, cross-circuit-type serialized graph representation, and design an analog-specific generative engine integrating graph neural networks with topology-aware encoding. Contributions/Results: The framework enables fully automated and scalable topology generation. It significantly improves topology diversity (+327%) and device density (+215%). Moreover, it synthesizes numerous high-performance, previously unreported circuit topologies, increasing the count of Pareto-optimal solutions in benchmark tasks by 4.8×. This work establishes a new paradigm for analog IC design—shifting from experience-driven to data- and model-coordinated automation.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Cognitive Modeling & Cognitive Systems: Analogy

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The massive and large-scale design of foundational semiconductor integrated circuits (ICs) is crucial to sustaining the advancement of many emerging and future technologies, such as generative AI, 5G/6G, and quantum computing. Excitingly, recent studies have shown the great capabilities of foundational models in expediting the design of digital ICs. Yet, applying generative AI techniques to accelerate the design of analog ICs remains a significant challenge due to critical domain-specific issues, such as the lack of a comprehensive dataset and effective representation methods for analog circuits. This paper proposes, $ extbf{AnalogGenie}$, a $underline{ extbf{Gen}}$erat$underline{ extbf{i}}$ve $underline{ extbf{e}}$ngine for automatic design/discovery of $underline{ extbf{Analog}}$ circuit topologies--the most challenging and creative task in the conventional manual design flow of analog ICs. AnalogGenie addresses two key gaps in the field: building a foundational comprehensive dataset of analog circuit topology and developing a scalable sequence-based graph representation universal to analog circuits. Experimental results show the remarkable generation performance of AnalogGenie in broadening the variety of analog ICs, increasing the number of devices within a single design, and discovering unseen circuit topologies far beyond any prior arts. Our work paves the way to transform the longstanding time-consuming manual design flow of analog ICs to an automatic and massive manner powered by generative AI. Our source code is available at https://github.com/xz-group/AnalogGenie.

Problem

• Addresses lack of comprehensive dataset for analog IC design.
• Develops scalable sequence-based graph representation for analog circuits.
• Automates discovery of novel analog circuit topologies using generative AI.

Innovation

• Generative engine for analog circuit design
• Comprehensive dataset for analog topologies
• Scalable sequence-based graph representation