Treffer: A comparative analysis of GPUs, TPUs, DPUs, and QPUs for deep learning with python.

In the rapidly evolving field of deep learning, the computational demands for training sophisticated models have escalated, prompting a shift towards specialized hardware accelerators such as graphics processing units (GPUs), tensor processing units (TPUs), data processing units (DPUs), and quantum processing units (QPUs). This article provides a comprehensive analysis of these heterogeneous computing architectures, highlighting their unique characteristics, performance metrics, and suitability for various deep learning tasks. By leveraging python, a predominant programming language in the data science domain, the integration and optimization techniques applicable to each hardware platform is explored, offering insights into their practical implications for deep learning research and application. The architectural differences that influence computational efficiency is examined, parallelism, and energy consumption, alongside discussing the evolving ecosystem of software tools and libraries that support deep learning on these platforms. Through a series of benchmarks and case studies, this study aims to equip researchers and practitioners with the knowledge to make informed decisions when selecting hardware for their deep learning projects, ultimately contributing to the acceleration of model development and innovation in the field. [ABSTRACT FROM AUTHOR]

Copyright of Indonesian Journal of Electrical Engineering & Computer Science is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)