Systematic Analysis of TPU – The Game changer of Machine Learning

DOI

10.2991/978-94-6463-986-5_47How to use a DOI?

Keywords

GPU; TPU; AI Hardware; Specialized Hardware; Computational Efficiency

Abstract

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 high-power 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.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zixuan Ye
PY  - 2026
DA  - 2026/02/18
TI  - Systematic Analysis of TPU – The Game changer of Machine Learning
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 458
EP  - 464
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_47
DO  - 10.2991/978-94-6463-986-5_47
ID  - Ye2026
ER  -