EXPLORING THE USE OF MACHINE LEARNING FOR ELECTRONIC DESIGN AUTOMATION

The application of machine learning to electronic design automation has shown promising results in overcoming the drawbacks of more conventional methods, such as higher design complexity, higher design costs, and longer design cycles. These downsides include higher design complexity, higher design costs, and longer design cycles. In this article, we provide an overview of the various machine learning algorithms and approaches that are currently in use in the field of electronic design automation. We offer a paradigm that incorporates supervised and unsupervised learning in addition to deep learning, reinforcement learning, transfer learning, explainable artificial intelligence, and evolutionary algorithms. We address a wide range of subjects, including the creation and tagging of data, the interpretability of models, scalability, interaction with already existing tools, and the optimisation of electrical system performance. In addition, we discuss the limitations and challenges of present approaches. The use of machine learning into the process of automating electrical design has the potential to improve the accuracy, speed, and overall efficiency of the procedure for creating and optimising electronic systems