Innovative VLSI System Design and Embedded Architectures Empowered by AI and Machine Learning Advancements

The purposes of this study are to merge Artificial Intelligence (AI) and Machine Learning (ML) technologies with the VLSI system design and embedded architectures to reduce the challenges of high complexity and high performance demands in modern electronics. The work extends to suggest new AI/ML driven algorithms for automation and optimization of VLSI design process so as to enhance power efficiency and system performance. To realize adaptive resource allocation, the RL based approach is utilized, a neural net is used to operate the circuit, and employed are machine learning algorithms to do real-time fault detection and predictive maintenance in embedded systems. This work addresses significant challenge such as strong power consumption, design scalability, and performance bottleneck and it seems that the traditional approaches are not able to overcome this problem. This work contributes one step towards design of smarter, more efficient embedded systems for the next generation of 6G communications, autonomous vehicles and IoT using AI/ML. These Aided VLSI and embedded systems will be both more adaptive and future ready, less development time, more energy efficient and also more adaptive.