Heterogeneous Computing: Integrating CPUs, GPUs, TPUs, and FPGAs for High-Performance AI Processing

Author(s)	Karthik Wali
Country	United States
Abstract	In parallel computing, we have a way of experiencing the growth of computational requirements of Artificial Intelligence (AI) and Machine Learning (ML) procedures known as heterogeneous computing. Standard computation structures, like CPUs, have failed to support large-scale computations for AI, thus the integration of other computation processors, including GPUs, TPUs, and FPGAs. This paper discusses the advantages and disadvantages of having heterogeneous computing architecture, especially in AI and deep learning systems. Combining SoC processor types can enhance High-performance AI surgery's effectiveness, adaptability, and energy use. We compare different architectures, describe practical solutions, and focus on various approaches to resource management. Additionally, we discuss the examples of AI in specific areas like picture and pattern recognition, NLP, and self-driving systems. By using the information obtained in this paper, it is possible to conclude that heterogeneous computing is a promising direction in the evolution of AI and to draw some expectations regarding further advancements in this field.
Field	Engineering
Published In	Volume 1, Issue 3, November-December 2019
Published On	2019-12-05
DOI	https://doi.org/10.36948/ijfmr.2019.v01i03.48991
Short DOI	https://doi.org/g9q32r

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research