A PCA Acceleration Algorithm For WiFi Sensing And Its Hardware Implementation

Currently, we are entering the wearable internet era. The use of WiFi signals to perceive human activities or changes in vital signs has gradually become a topic that people are enthusiastic about. Principal Component Analysis (PCA), as a universal data dimensionality reduction algorithm, has been widely applied in the field of WiFi sensing. It is utilized to expedite data processing time and enhance real-time detection capabilities. This paper proposes an acceleration algorithm for PCA in the field of WiFi sensing along with its corresponding hardware architecture. The experimental results indicate that the accuracy of this algorithm can reach 0.9965, and the processing time is approximately 10 ms. Based on the TSMC 22nm technology, the Design Complier (DC) results show that the data throughput of this hardware architecture can reach 24 Gbps@800M, with a gate count of 7571, and the power consumption is 1.6321mW.