Luca Greco, Francesco Moscato, Pierluigi Ritrovato, Mario Vento
{"title":"基于fpga的心律失常CNN快速低成本检测体系结构","authors":"Luca Greco, Francesco Moscato, Pierluigi Ritrovato, Mario Vento","doi":"10.1016/j.iot.2025.101705","DOIUrl":null,"url":null,"abstract":"<div><div>Deep Neural Networks have been applied in many fields and have exhibited extraordinary abilities. However, many challenges arise when dealing with embedded or low-resource computing architectures in different contexts like healthcare or IoT in Industry 4.0. In recent years, rapid growth has been seen in using machine learning techniques to interpret sensor data in healthcare applications. Convolutional Neural Networks (CNNs) are highly effective, but they have a significant drawback: they require large amounts of computational resources, usually available only “on the Cloud”. Edge and Fog nodes in healthcare applications (e.g. wearable sensors) are generally ill-suited to running CNN models with requirements like low computational resources, real-time execution, (very) low power consumption or low intrusiveness. In order to get through these difficulties, we propose a solution based on novel data-flow architectures and layer partitioning that enables fast classification in CNNs even when dealing with low resources. We apply our approach in developing a classifier (based on CNNs) for arrhythmia detection, which maintains good precision on low-power and low-cost FPGAs. We prove that the presented approach is general enough to distribute computation on parallel FPGAs too. Results show interesting performance improvements even when using low-resource hardware to implement the classifier.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101705"},"PeriodicalIF":7.6000,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fast and low cost FPGA-based architecture for arrhythmia detection with CNN\",\"authors\":\"Luca Greco, Francesco Moscato, Pierluigi Ritrovato, Mario Vento\",\"doi\":\"10.1016/j.iot.2025.101705\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Deep Neural Networks have been applied in many fields and have exhibited extraordinary abilities. However, many challenges arise when dealing with embedded or low-resource computing architectures in different contexts like healthcare or IoT in Industry 4.0. In recent years, rapid growth has been seen in using machine learning techniques to interpret sensor data in healthcare applications. Convolutional Neural Networks (CNNs) are highly effective, but they have a significant drawback: they require large amounts of computational resources, usually available only “on the Cloud”. Edge and Fog nodes in healthcare applications (e.g. wearable sensors) are generally ill-suited to running CNN models with requirements like low computational resources, real-time execution, (very) low power consumption or low intrusiveness. In order to get through these difficulties, we propose a solution based on novel data-flow architectures and layer partitioning that enables fast classification in CNNs even when dealing with low resources. We apply our approach in developing a classifier (based on CNNs) for arrhythmia detection, which maintains good precision on low-power and low-cost FPGAs. We prove that the presented approach is general enough to distribute computation on parallel FPGAs too. Results show interesting performance improvements even when using low-resource hardware to implement the classifier.</div></div>\",\"PeriodicalId\":29968,\"journal\":{\"name\":\"Internet of Things\",\"volume\":\"33 \",\"pages\":\"Article 101705\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2025-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internet of Things\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2542660525002197\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2542660525002197","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Fast and low cost FPGA-based architecture for arrhythmia detection with CNN
Deep Neural Networks have been applied in many fields and have exhibited extraordinary abilities. However, many challenges arise when dealing with embedded or low-resource computing architectures in different contexts like healthcare or IoT in Industry 4.0. In recent years, rapid growth has been seen in using machine learning techniques to interpret sensor data in healthcare applications. Convolutional Neural Networks (CNNs) are highly effective, but they have a significant drawback: they require large amounts of computational resources, usually available only “on the Cloud”. Edge and Fog nodes in healthcare applications (e.g. wearable sensors) are generally ill-suited to running CNN models with requirements like low computational resources, real-time execution, (very) low power consumption or low intrusiveness. In order to get through these difficulties, we propose a solution based on novel data-flow architectures and layer partitioning that enables fast classification in CNNs even when dealing with low resources. We apply our approach in developing a classifier (based on CNNs) for arrhythmia detection, which maintains good precision on low-power and low-cost FPGAs. We prove that the presented approach is general enough to distribute computation on parallel FPGAs too. Results show interesting performance improvements even when using low-resource hardware to implement the classifier.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
The journal will place a high priority on timely publication, and provide a home for high quality.
Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.