Implementation of Machine Learning in VLSI Integrated Circuit Design

• Published in: SN computer science Vol. 4; no. 2; p. 137
• Main Authors: Shreyanth, S., Harshitha, D. S., Niveditha, S.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.03.2023; Springer Nature B.V
• Subjects: Advances in Computational Intelligence for Artificial Intelligence; Artificial intelligence; Automation; Circuit design; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Design; Electronic design automation; False alarms; Identification; Information Systems and Communication Service; Integrated circuits; Internet of Things and Data Analytics; Machine Learning; Original Research; Pattern Recognition and Graphics; R&D; Research & development; Semiconductors; Silicon; Software; Software Engineering/Programming and Operating Systems; Very large scale integration; Vision; Integrated circuits; VLSI; Convolution neural network; Machine learning; Hardware computation; Designing; Optimization; CAD tools
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-022-01580-5

Machine learning has made an impact on the area attributed to microchip, and it is initially used in automation. These techniques will eventually supplant the current VLSI design concept. Design creation has been automated by substituting time-consuming traditional concepts developed by experts. This development could result in a tremendous change in the realm of hardware computation and AI’s powerful analysis tools. As a result, during the last four decades, several tasks have been computerized, and a plethora of sophisticated tasks have been mechanized. And then if someone has invented a new concept (in terms of computing, analyzing, optimization, and inter-relationship manufacturing) and the invention process is computerized, major firms like IBM and Intel have their own CAD division to handle these issues for design automation. Several firms, including major electronic design automation company, sell CAD software’s based on the employment of intelligent tools at circuit development. ML had broadened the present reach by assisting with plausible solutions for an extensive kind on the subject problems as well as challenges in a different range of technical domains. Importance of machine learning in the EDA solutions industry has increased its capabilities by lowering the man-hours spent on design confirmation and its implementation, cost reductions, and design product productivity. In this article we have discussed the importance of the application of machine learning in VLSI chip design and development and how we implemented ML-oriented BIST.