Evolving Computer Architectures for AI-Intensive Workloads: Challenges and Innovations

Abstract

Abstract: In the era of rapid advancements in artificial intelligence (AI), evolving computer architectures play a pivotal role in meeting the demands of AI-intensive workloads. This paper explores the challenges and innovations associated with designing computer architectures tailored for AI applications. Firstly, we examine the growing complexity and scale of AI models, which pose significant challenges in terms of computational efficiency, memory bandwidth, and power consumption. Next, we discuss the emergence of specialized hardware accelerators, such as graphics processing units (GPUs), tensor processing units (TPUs), and neuromorphic chips, designed to optimize AI workloads. Additionally, we explore novel architectural paradigms, including heterogeneous computing, in-memory computing, and reconfigurable architectures, which aim to address the unique requirements of AI algorithms. Furthermore, we investigate the role of software-hardware co-design methodologies in optimizing performance and energy efficiency for AI tasks. Despite significant progress, several challenges remain, including the need for scalable and programmable architectures, efficient memory hierarchy designs, and effective utilization of emerging technologies such as quantum computing and photonic computing. By addressing these challenges and embracing innovations in computer architecture, we can unlock the full potential of AI technologies and drive transformative advances in various domains, including healthcare, finance, autonomous systems, and beyond.