Systematic Analysis of TPU – The Game changer of Machine Learning

In the era of rapid advancements in artificial intelligence (AI) and deep learning, the choice of hardware processors plays a critical role in determining the efficiency of model training and inference.This paper talks about the two leading processors: the general purposed Graphics Processing Unit (GPU) and the specialized Tensor Processing Unit (TPU) and compares their uses in AI deep learning.Whilst NVIDIA's GPUs were originally designed for rendering graphics, they were successfully repurposed for training an AI model.This paper explains how tensor cores were introduced and allows much faster parallel computations.However, GPU's versatile architecture leads to significant inefficiencies for deep learning tasks, often wasting energy and having a highpower consumption.Therefore, this shows that GPUs are not the best choice for AI deep learning.Then, the essay discusses Google's TPUs, which were built specifically for AI.This specialized design makes them much faster and more energy-efficient for training large AI models.The text describes how TPUs have improved over several generations and shows how the TPU delivers superior performance-per-watt for large scale training and inference.