International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Case Studies | Science and Technology | India | Volume 12 Issue 1, January 2023 | Rating: 4.9 / 10


Evolving CPU Architectures for AI

S. Tharun Anand Reddy


Abstract: Artificial Intelligence (AI) has revolutionized the tech industry, transforming the way we interact with our surroundings, work, and live. However, AI's data and math - intensive operations require specialized hardware optimizations. As AI becomes more widespread, CPU architectures must adapt to handle these unique computational demands. This article will explore the key CPU architectural advancements responsible for accelerating AI inferencing and training. We will discuss the shift towards domain - specific architectures, the integration of dedicated AI accelerators, and innovations in memory and interconnects. Domain - specific architectures have become especially significant in AI. General - purpose CPUs are not capable of handling the complex computations required for AI. Consequently, domain - specific architectures such as graphics processing units (GPUs) and field - programmable gate arrays (FPGAs) have emerged as the go - to hardware for AI workloads. In addition to domain - specific architectures, dedicated AI accelerators have also gained traction in recent years. These accelerators are custom - built for AI workloads and can significantly boost performance. Examples of dedicated AI accelerators include Google's Tensor Processing Units (TPUs) and Nvidia's Tensor Cores. Moreover, innovations in memory and interconnects have played a crucial role in enabling accelerated AI inferencing and training. One such innovation is High Bandwidth Memory (HBM), which provides a high - speed interface between the CPU and GPU. Another innovation is using interconnects, such as the Cache Coherent Interconnect for Accelerators (CCIX), which enables efficient communication between the CPU and accelerators.


Keywords: CPU architecture, artificial intelligence, machine learning, deep learning, accelerators, heterogeneous computing


Edition: Volume 12 Issue 1, January 2023,


Pages: 1238 - 1242


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