AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Design

TL;DR

AICircuit provides a diverse, multi-level dataset and benchmark for applying machine learning to complex analog and RF circuit design, addressing data scarcity and evaluation challenges in the field.

Contribution

This work introduces a comprehensive dataset and benchmark for ML-driven analog and RF circuit design, including complex systems and evaluation metrics.

Findings

ML algorithms can effectively learn design-to-parameter mappings

The dataset covers both simple and complex circuit configurations

Evaluation reveals potential and limitations of current ML methods

Abstract

Analog and radio-frequency circuit design requires extensive exploration of both circuit topology and parameters to meet specific design criteria like power consumption and bandwidth. Designers must review state-of-the-art topology configurations in the literature and sweep various circuit parameters within each configuration. This design process is highly specialized and time-intensive, particularly as the number of circuit parameters increases and the circuit becomes more complex. Prior research has explored the potential of machine learning to enhance circuit design procedures. However, these studies primarily focus on simple circuits, overlooking the more practical and complex analog and radio-frequency systems. A major obstacle for bearing the power of machine learning in circuit design is the availability of a generic and diverse dataset, along with robust metrics, which are essential for thoroughly evaluating and improving machine learning algorithms in the analog and radio-frequency circuit domain. We present AICircuit, a comprehensive multi-level dataset and benchmark for developing and evaluating ML algorithms in analog and radio-frequency circuit design. AICircuit comprises seven commonly used basic circuits and two complex wireless transceiver systems composed of multiple circuit blocks, encompassing a wide array of design scenarios encountered in real-world applications. We extensively evaluate various ML algorithms on the dataset, revealing the potential of ML algorithms in learning the mapping from the design specifications to the desired circuit parameters.