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A high-performance, hardware-based deep learning system for disease diagnosis

Modern deep learning schemes have shown human-level performance in the area of medical science. However, the implementation of deep learning algorithms on dedicated hardware remains a challenging task because modern algorithms and neuronal activation functions are generally not hardware-friendly and...

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Autores principales: Siddique, Ali, Iqbal, Muhammad Azhar, Aleem, Muhammad, Lin, Jerry Chun-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454880/
https://www.ncbi.nlm.nih.gov/pubmed/36091996
http://dx.doi.org/10.7717/peerj-cs.1034
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author Siddique, Ali
Iqbal, Muhammad Azhar
Aleem, Muhammad
Lin, Jerry Chun-Wei
author_facet Siddique, Ali
Iqbal, Muhammad Azhar
Aleem, Muhammad
Lin, Jerry Chun-Wei
author_sort Siddique, Ali
collection PubMed
description Modern deep learning schemes have shown human-level performance in the area of medical science. However, the implementation of deep learning algorithms on dedicated hardware remains a challenging task because modern algorithms and neuronal activation functions are generally not hardware-friendly and require a lot of resources. Recently, researchers have come up with some hardware-friendly activation functions that can yield high throughput and high accuracy at the same time. In this context, we propose a hardware-based neural network that can predict the presence of cancer in humans with 98.23% accuracy. This is done by making use of cost-efficient, highly accurate activation functions, Sqish and LogSQNL. Due to its inherently parallel components, the system can classify a given sample in just one clock cycle, i.e., 15.75 nanoseconds. Though this system is dedicated to cancer diagnosis, it can predict the presence of many other diseases such as those of the heart. This is because the system is reconfigurable and can be programmed to classify any sample into one of two classes. The proposed hardware system requires about 983 slice registers, 2,655 slice look-up tables, and only 1.1 kilobits of on-chip memory. The system can predict about 63.5 million cancer samples in a second and can perform about 20 giga-operations per second. The proposed system is about 5–16 times cheaper and at least four times speedier than other dedicated hardware systems using neural networks for classification tasks.
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spelling pubmed-94548802022-09-09 A high-performance, hardware-based deep learning system for disease diagnosis Siddique, Ali Iqbal, Muhammad Azhar Aleem, Muhammad Lin, Jerry Chun-Wei PeerJ Comput Sci Bioinformatics Modern deep learning schemes have shown human-level performance in the area of medical science. However, the implementation of deep learning algorithms on dedicated hardware remains a challenging task because modern algorithms and neuronal activation functions are generally not hardware-friendly and require a lot of resources. Recently, researchers have come up with some hardware-friendly activation functions that can yield high throughput and high accuracy at the same time. In this context, we propose a hardware-based neural network that can predict the presence of cancer in humans with 98.23% accuracy. This is done by making use of cost-efficient, highly accurate activation functions, Sqish and LogSQNL. Due to its inherently parallel components, the system can classify a given sample in just one clock cycle, i.e., 15.75 nanoseconds. Though this system is dedicated to cancer diagnosis, it can predict the presence of many other diseases such as those of the heart. This is because the system is reconfigurable and can be programmed to classify any sample into one of two classes. The proposed hardware system requires about 983 slice registers, 2,655 slice look-up tables, and only 1.1 kilobits of on-chip memory. The system can predict about 63.5 million cancer samples in a second and can perform about 20 giga-operations per second. The proposed system is about 5–16 times cheaper and at least four times speedier than other dedicated hardware systems using neural networks for classification tasks. PeerJ Inc. 2022-07-19 /pmc/articles/PMC9454880/ /pubmed/36091996 http://dx.doi.org/10.7717/peerj-cs.1034 Text en ©2022 Siddique et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Siddique, Ali
Iqbal, Muhammad Azhar
Aleem, Muhammad
Lin, Jerry Chun-Wei
A high-performance, hardware-based deep learning system for disease diagnosis
title A high-performance, hardware-based deep learning system for disease diagnosis
title_full A high-performance, hardware-based deep learning system for disease diagnosis
title_fullStr A high-performance, hardware-based deep learning system for disease diagnosis
title_full_unstemmed A high-performance, hardware-based deep learning system for disease diagnosis
title_short A high-performance, hardware-based deep learning system for disease diagnosis
title_sort high-performance, hardware-based deep learning system for disease diagnosis
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454880/
https://www.ncbi.nlm.nih.gov/pubmed/36091996
http://dx.doi.org/10.7717/peerj-cs.1034
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