Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/129821
Título: MPSoC FPGA Implementation of Algorithms of Machine Learning for Clinical Applications Using High-Level Design Methodology
Autores/as: Guanche Hernández, Mario Daniel 
León, Raquel 
Carballo, Pedro P. 
Clasificación UNESCO: 3314 Tecnología médica
Palabras clave: Fpga
Hls
Hyperspectral Imaging
K-Means
Mpsoc, et al.
Fecha de publicación: 2023
Conferencia: 26th Euromicro Conference on Digital System Design (DSD 2023) 
Resumen: This paper presents the design of an FPGA-accelerated application for skin cancer detection which uses both hyperspectral imaging and a k-means algorithm. The accelerator is designed employing 3 FPGA kernels. The first 2 kernels filter and normalize the hyperspectral image. Then, the last kernel runs k-means to segment the image into three different regions according to the distribution of the lesion. This application is developed following the HLS methodology, implemented as an embedded system in MPSoC, and runs under Linux OS. FPGA acceleration will improve the application's throughput and energy efficiency significantly when compared to pure software execution.
URI: http://hdl.handle.net/10553/129821
ISBN: 9798350344196
DOI: 10.1109/DSD60849.2023.00109
Fuente: Proceedings - 2023 26th Euromicro Conference on Digital System Design, DSD 2023[EISSN ], p. 764-769, (Enero 2023)
Colección:Actas de congresos
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