Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation
This addresses data scarcity for engineers and researchers in electronic design automation, though it is an incremental application of existing diffusion models to a new domain.
This study tackled the problem of limited training data for machine learning models in digital VLSI circuit design by using diffusion models to generate synthetic circuit data from HSPICE simulations with 22nm CMOS technology. The results showed that the synthetic data closely resembled real data and effectively enhanced predictive analysis accuracy through data augmentation.
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 certainly effective in predictive analysis of VLSI design for digital circuits.