Engineering AI Accelerators: GPUs, TPUs, and NPUs

Abstract

Artificial intelligence requires specialized hardware to perform the large number of mathematical calculations needed for training and inference. This paper examines three major types of AI accelerators: Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and Neural Processing Units (NPUs). Each processor is designed to improve AI performance in different ways. GPUs are widely used due to their parallel processing performance and flexibility. TPUs are specialized for machine learning workloads and matrix operations. NPUs are designed for efficient processing on laptops and cell phones. This paper explains how each accelerator works, the advantages and disadvantages of each accelerator, and how each plays a role in modern AI systems. Overall, specialized AI hardware allows artificial intelligence to operate faster and more efficiently compared with relying on CPUs alone.