Physical AI and Intelligent Edge Processing: Transforming Future Computing Systems

Abstract

Physical AI (PAI) is an emerging paradigm in which artificial intelligence models are integrated directly into hardware systems to perform computation in a more energy-efficient and faster manner. Coupled with intelligent edge processing, PAI enables real-time decision-making in Internet-of-Things (IoT) devices, autonomous systems, and industrial automation, overcoming limitations of cloud-centric architectures. This paper provides a comprehensive review of physical AI systems, their architectures, applications, and the integration of intelligent edge processing. Key challenges such as energy constraints, hardware limitations, and scalability are discussed. The study also highlights future directions, including neuromorphic computing, analog AI accelerators, and hybrid edge-cloud systems. Our analysis shows that physical AI with intelligent edge processing can substantially improve system latency, security, and resilience while reducing overall computational costs.

KEYWORDS: Physical AI, Intelligent Edge, Edge Computing, Neuromorphic Systems, IoT, AI Hardware Acceleration