Integrating Deep Learning into VLSI Technology: Challenges and Opportunities

Abstract

This paper conducts a comprehensive review and analysis of the difficulties and possibilities related to integrating deep learning algorithms into the future of VLSI design and technology. The area of integrated circuit design is becoming increasingly complex as transistors become smaller and the expectations for enhanced reliability and environmental sustainability increase. Analysts are looking into novel techniques that involve deep learning, as traditional techniques find it challenging to tackle these issues. In particular, deep neural networks possess the ability to improve various aspects of integrated circuit design, including timing assessment, layout enhancement, fault detection, and energy utilization minimization. Deep learning has become a viable solution for addressing a range of VLSI challenges, providing opportunities for automated processes, enhancement, and creativity at several phases of the development and fabrication cycle. The incorporation of deep learning into system acceleration, identifying defects, layout synthesis, and future repairs is investigated in this article. It also draws attention to the challenges and opportunities associated with incorporating neural networks into VLSI, highlighting the necessity of multidisciplinary cooperation and creativity to realize their maximum potential. By surmounting these challenges and capitalizing on the prospects presented by deep computing, the integrated circuit sector might unleash unprecedented heights of efficiency, productivity, and inventiveness in integrated circuit innovations.