Real-Time and Non-Contact Arrhythmia Recognition Algorithm for Hardware Implementation

Kai Lei, Ming-Yueh Ku, Shuenn-Yuh Lee
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Abstract

The purpose of the system is to establish a real-time arrhythmia recognition according to image, which can be easily implemented by hardware as artificial intelligence (AI) accelerator. Through the remote photoplethysmography (rPPG), the slight changes of the face are captured in a non-contact way, and the analysis of the AI algorithm can deduce the correlation between subtle change of the face and arrhythmia. The design of a conventional neural network has a large of multipliers and adders in the internal network, and multi-bit multipliers and adders usually cause a long critical path. Through the accelerated design based on the computer in memory (CIM) system, the time of transferring the data can be effectively reduced. While the high-precision network also has a lot of parameters, so we need to compress the model for the realization of hardware.
实时非接触心律失常识别算法的硬件实现
该系统的目的是建立一种基于图像的实时心律失常识别系统,该系统可以很容易地通过硬件作为人工智能(AI)加速器来实现。通过远程光电容积脉搏波(rPPG),以非接触的方式捕捉面部的细微变化,通过AI算法的分析,可以推断出面部细微变化与心律失常之间的相关性。传统的神经网络设计在内部网络中有大量的乘法器和加法器,而多比特乘法器和加法器通常会导致较长的关键路径。通过基于内存计算机(CIM)系统的加速设计,可以有效地减少数据传输时间。而高精度网络也有很多参数,因此我们需要压缩模型以便硬件实现。
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