The rise of machine learning (ML) and artificial intelligence (AI) have instigated significant interests in developing domain specific integrated circuits and architectures that can support the computational demands of AI. These chips are commonly known as AI chips. AI chips, both for training and inferencing, have emerged out to be new areas of research and commercialization in semiconductor industry. Particularly, increasing emphasis has been placed on developing low-power AI chips for edge applications where AI can be local, distributed, and seamlessly integrated in sensors or other internet of things (IoT) devices for building naturally-autonomous systems. On the other-hand, globalization of semiconductor ecosystem has led to an era where achieving dominance on Microelectronics supply chain has become as exciting as the famous series “Game of Thrones” for not only the companies but also the countries. Furthermore, the end of Moore’s Law of Scaling of traditional semiconductor transistors has added the climax to this situation as the development and manufacturing of advanced process nodes for the next generation of logic and memory devices get expensive driving most semiconductor industry in the US to be fabless. Interestingly, advanced process nodes such as FINFETS, 3D Transistors, RRAMs/Memristors, and 3D/Monolithic integration and packaging approaches provide unsurpassed opportunities to achieve the much desired On-Chip AI along with the primitives for implementing Hardware Security, and Trust into the integrated circuits. This talk will review some of these technologies and our research efforts in these areas.