Chip Architecture for AI - A Comprehensive Survey of Design Principles, Technologies, and Implementations

This paper presents a comprehensive survey of hardware architectures and deployment platforms for artificial intelligence (AI) workloads. The rapid advancement of AI technologies has led to the development of specialized hardware accelerators, cloud-based services, and edge computing devices tailored for AI and machine learning (ML) tasks. We discuss a wide range of hardware architectures, including GPUs, TPUs, CPUs, ASICs, FPGAs, hybrid chips, neuromorphic computing, quantum computing, and high-performance computing (HPC) clusters. For each architecture, we explore its design principles, advantages, disadvantages, and suitability for different AI applications. Additionally, we analyze deployment platforms such as dedicated AI processors, cloud AI services, and edge AI devices, discussing their features, benefits, and considerations for adoption. By providing insights into the diverse landscape of AI hardware architectures and deployment platforms, this survey aims to assist researchers, practitioners, and decision-makers in selecting the most appropriate hardware solutions for their AI projects and applications.