Emerging VLSI Technologies for High performance AI and ML Applications

The capabilities of artificial intelligence (AI) and machine learning (ML) algorithms are constantly expanding, necessitating efficient and high-performance hardware systems. We have investigated the creation of hardware accelerators based on VLSI that are intended to effectively manage the heavy workloads of machine learning jobs, also explored low-power VLSI architectures that preserve computing capabilities while reducing power consumption to solve energy efficiency issues in AI and ML systems. To balance performance and energy utilization, power management strategies and circuit design improvements are analysed. The study emphasizes hardware-software co-design techniques, considering the integration of VLSI-based hardware accelerators with software frameworks to obtain optimal performance and flexibility, to address the complexity and scalability of AI and ML systems. We also examined the cutting-edge VLSI technologies that have the potential to support powerful AI and ML applications. The speed and effectiveness of AI and ML algorithms could be improved significantly by these technologies, which include neuromorphic computing, approximation computing, and in-memory computing. The research also discusses the difficulties in designing VLSI for AI and ML algorithms, and its possible solutions for challenges including design complexity, scalability, memory management, and data mobility.