Qualitative Data Augmentation for Performance Prediction in VLSI circuits

TL;DR

This paper demonstrates that using GAN-generated artificial data can significantly enhance machine learning model accuracy in VLSI circuit performance prediction, especially when training data is scarce.

Contribution

It introduces a novel approach of applying GANs to generate synthetic circuit data, improving ML model accuracy in VLSI design automation.

Findings

Artificial data reduces prediction error by over 50%.

GAN-generated data improves ML model performance with limited original data.

Method applicable to various analog and digital circuits.

Abstract

Various studies have shown the advantages of using Machine Learning (ML) techniques for analog and digital IC design automation and optimization. Data scarcity is still an issue for electronic designs, while training highly accurate ML models. This work proposes generating and evaluating artificial data using generative adversarial networks (GANs) for circuit data to aid and improve the accuracy of ML models trained with a small training data set. The training data is obtained by various simulations in the Cadence Virtuoso, HSPICE, and Microcap design environment with TSMC 180nm and 22nm CMOS technology nodes. The artificial data is generated and tested for an appropriate set of analog and digital circuits. The experimental results show that the proposed artificial data generation significantly improves ML models and reduces the percentage error by more than 50\% of the original percentage error, which were previously trained with insufficient data. Furthermore, this research aims to contribute to the extensive application of AI/ML in the field of VLSI design and technology by relieving the training data availability-related challenges.