Fast and low cost FPGA-based architecture for arrhythmia detection with CNN

IF 7.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Luca Greco, Francesco Moscato, Pierluigi Ritrovato, Mario Vento
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引用次数: 0

Abstract

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.
基于fpga的心律失常CNN快速低成本检测体系结构
深度神经网络在许多领域得到了应用,并表现出非凡的能力。然而,在不同的环境(如工业4.0中的医疗保健或物联网)中处理嵌入式或低资源计算架构时,会出现许多挑战。近年来,在医疗保健应用中使用机器学习技术来解释传感器数据的快速增长。卷积神经网络(cnn)非常有效,但它们有一个明显的缺点:它们需要大量的计算资源,通常只能在“云”上使用。医疗保健应用程序(例如可穿戴传感器)中的边缘和雾节点通常不适合运行CNN模型,这些模型要求低计算资源、实时执行、(非常)低功耗或低侵入性。为了克服这些困难,我们提出了一种基于新颖数据流架构和层划分的解决方案,使cnn在处理低资源时也能快速分类。我们将该方法应用于心律失常检测分类器的开发(基于cnn),该分类器在低功耗和低成本fpga上保持良好的精度。我们证明了该方法的通用性,足以在并行fpga上分配计算。结果显示,即使在使用低资源硬件实现分类器时,性能也有了有趣的改进。
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来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
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.
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