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

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.