Template Matching Based Early Exit CNN for Energy-efficient Myocardial Infarction Detection on Low-power Wearable Devices
نویسندگان
چکیده
Myocardial Infarction (MI), also known as heart attack, is a life-threatening form of disease that leading cause death worldwide. Its recurrent and silent nature emphasizes the need for continuous monitoring through wearable devices. The device solutions should provide adequate performance while being resource-constrained in terms power memory. This paper proposes an MI detection methodology using Convolutional Neural Network (CNN) outperforms state-of-the-art works on devices two datasets - PTB PTB-XL, energy memory-efficient. Moreover, we propose novel Template Matching based Early Exit (TMEX) CNN architecture further increases efficiency compared to baseline maintaining similar performance. Our TMEX achieve 99.33% 99.24% accuracy dataset, whereas PTB-XL dataset they 84.36% 84.24% accuracy, respectively. Both architectures are suitable requiring only 20 KB RAM. Evaluation real hardware shows our 0.6x 53x more energy-efficient than improves by 8.12% (PTB) 6.36% (PTB-XL) architecture.
منابع مشابه
application of upfc based on svpwm for power quality improvement
در سالهای اخیر،اختلالات کیفیت توان مهمترین موضوع می باشد که محققان زیادی را برای پیدا کردن راه حلی برای حل آن علاقه مند ساخته است.امروزه کیفیت توان در سیستم قدرت برای مراکز صنعتی،تجاری وکاربردهای بیمارستانی مسئله مهمی می باشد.مشکل ولتاژمثل شرایط افت ولتاژواضافه جریان ناشی از اتصال کوتاه مدار یا وقوع خطا در سیستم بیشتر مورد توجه می باشد. برای مطالعه افت ولتاژ واضافه جریان،محققان زیادی کار کرده ...
15 صفحه اولFast and Energy-Efficient CNN Inference on IoT Devices
Convolutional Neural Networks (CNNs) exhibit remarkable performance in various machine learning tasks. As sensor-equipped internet of things (IoT) devices permeate into every aspect of modern life, it is increasingly important to run CNN inference, a computationally intensive application, on resource constrained devices. We present a technique for fast and energy-efficient CNN inference on mobi...
متن کاملLPD: Low Power Display Mechanism for Mobile and Wearable Devices
A plethora of mobile devices such as smartphones, wearables, and tablets have been explosively penetrated into the market in the last decade. In battery powered mobile devices, energy is a scarce resource that should be carefully managed. A mobile device consists of many components and each of them contributes to the overall power consumption. This paper focuses on the energy conservation probl...
متن کاملLow Power Design for Future Wearable and Implantable Devices
With the fast progress in miniaturization of sensors and advances in micromachinery systems, a gate has been opened to the researchers to develop extremely small wearable/implantable microsystems for different applications. However, these devices are reaching not to a physical limit but a power limit, which is a critical limit for further miniaturization to develop smaller and smarter wearable/...
متن کاملCmos Based Thermal Energy Generator for Low Power Devices
This paper presents a thermal energy generator (TEG) designed using complementary metal oxide semiconductor (CMOS) process which converts thermal energy into electrical power. Energy harvesting techniques provide viable option to improve battery performance of low power devices. TEGs are of special interest due to its energy efficient, have no moving part and free maintenance. Secondly, thermal...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies
سال: 2022
ISSN: ['2474-9567']
DOI: https://doi.org/10.1145/3534580