Abstract
In the past few decades, machine learning, a subset of artificial intelligence (AI), has emerged as a disruptive technology which is now being extensively used and has stretched across various domains. Among the numerous applications, one of the most significant advancements due to Machine Learning is in the field of Very Large Scale Integrated Circuits (VLSI). Further growth and improvements in this field are highly anticipated in the near future. The fabrication of thousands of transistors in VLSI is time consuming and complex which demanded the automation of design process, and hence, computer-aided design (CAD) tools and technologies have started to evolve. The incorporation of machine learning in VLSI involves the application of machine learning algorithms at different abstraction levels of VLSI CAD. In this paper, we summarize several machine learning algorithms that have been developed and are being widely used. We also have briefly discussed about how machine learning methods have transuded the layers of VLSI design process from register transfer level (RTL) assertion generation to static timing analysis (STA) with smart and efficient models and methodologies, further enhancing the quality of chip design with power, performance and area improvements and complexity and turnaround time reduction.
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Bansal, M., Priya (2022). Machine Learning Perspective in VLSI Computer-Aided Design at Different Abstraction Levels. In: Shakya, S., Bestak, R., Palanisamy, R., Kamel, K.A. (eds) Mobile Computing and Sustainable Informatics. Lecture Notes on Data Engineering and Communications Technologies, vol 68. Springer, Singapore. https://doi.org/10.1007/978-981-16-1866-6_6
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DOI: https://doi.org/10.1007/978-981-16-1866-6_6
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