Open AccessVery‐large‐scale integration implementation of a convolutional neural network accelerator for abnormal heartbeat detection
Author(s) -
Chen Y.H.,
Juan Y.
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.3752
Subject(s) - heartbeat , convolutional neural network , computer science , chip , power (physics) , process (computing) , real time computing , electronic engineering , artificial intelligence , pattern recognition (psychology) , embedded system , computer hardware , engineering , telecommunications , physics , computer security , quantum mechanics , operating system
In this study, a very‐large‐scale integration implementation of a convolutional neural network (CNN) inference for abnormal heartbeat detection was proposed. Four‐lead electrocardiogram signals were used to detect abnormal heartbeat conditions, such as premature ventricular complex. 1D CNNs and fully connected layers were utilised in the proposed chip to achieve high‐speed, small‐area, and high‐accuracy arrhythmia detection. The proposed chip was implemented using a 90‐nm complementary metal‐oxide‐semiconductor process and operated at 125 MHz with a 0.67mm 2 core area. The power consumption was 4.18mW at high‐speed operation frequency (125 MHz) and 3.79μ W at 10kHz for low‐power applications. The detection accuracy was 95.14 % based on the MIT‐BIH arrhythmia database. Consequently, the properties of high speed, low power, small area, and high accuracy were established in the proposed accelerator chip.