Machine learning algorithms for FPGA Implementation in biomedical engineering applications: A review.

Autor:	Altman MB; Department of Energy Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 1591634311, Iran., Wan W; Department of Mechanical Engineering, University of New Mexico, MSC01 1150, Albuquerque, NM 87131, USA., Hosseini AS; Department of Electrical and Biomedical Engineering, Islamic Azad University, Golestan, Iran., Arabi Nowdeh S; Golestan Technical and Vocational Training Center, Gorgan, Iran., Alizadeh M; Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, 1591634311,Iran.
Jazyk:	angličtina
Zdroj:	Heliyon [Heliyon] 2024 Feb 18; Vol. 10 (4), pp. e26652. Date of Electronic Publication: 2024 Feb 18 (Print Publication: 2024).
DOI:	10.1016/j.heliyon.2024.e26652
Abstrakt:	Field Programmable Gate Arrays (FPGAs) are integrated circuits that can be configured by the user after manufacturing, making them suitable for customized hardware prototypes, a feature not available in general-purpose processors in Application Specific Integrated Circuits (ASIC). In this paper, we review the vast Machine Learning (ML) algorithms implemented on FPGAs to increase performance and capabilities in healthcare technology over 2001-2023. In particular, we focus on real-time ML algorithms targeted to FPGAs and hybrid System-on-a-chip (SoC) FPGA architectures for biomedical applications. We discuss how previous works have customized and optimized their ML algorithm and FPGA designs to address the putative embedded systems challenges of limited memory, hardware, and power resources while maintaining scalability to accommodate different network sizes and topologies. We provide a synthesis of articles implementing classifiers and regression algorithms, as they are significant algorithms that cover a wide range of ML algorithms used for biomedical applications. This article is written to inform the biomedical engineering and FPGA design communities to advance knowledge of FPGA-enabled ML accelerators for biomedical applications. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2024 Published by Elsevier Ltd.)
Databáze:	MEDLINE
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