Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation

NeurIPS 2023 Workshop MLSys Submission28 Authors

Published: 28 Oct 2023, Last Modified: 12 Dec 2023MlSys Workshop NeurIPS 2023 PosterEveryoneRevisionsBibTeX
Keywords: VLSI, systems, diffusion
TL;DR: Data augmentation for VLSI circuit design
Abstract: Generative AI has seen remarkable growth over the past few years, with diffusion models being state-of-the-art for image generation. This study investigates the use of diffusion models in generating artificial data generation for electronic circuits for enhancing the accuracy of subsequent machine learning models in tasks such as performance assessment, design, and testing when training data is usually known to be very limited. We utilize simulations in the HSPICE design environment with 22nm CMOS technology nodes to obtain representative real training data for our proposed diffusion model. Our results demonstrate the close resemblance of synthetic data using diffusion model to real data. We validate the quality of generated data, and demonstrate that data augmentation is certainly effective in predictive analysis of VLSI design for digital circuits.
Submission Number: 28