FPGA-Based Implementation of Convolutional Neural Networks for Enhanced Physical Security in Data Center Door Access Systems

Abstract

Digital data security has become a critical topic, whereas the physical security of the hardware on which digital data is stored remains equally critical. The loss of a server is equivalent to the loss of data. Access to the data center rooms is provided using badges assigned to each individual. However, these badges are at risk of being lost or stolen, which can lead to unauthorized access. This represents a risk of the loss of physical equipment and, consequently, of the loss of confidential data. This study proposes an intelligent security system for access doors in data center rooms to ensure optimal security measures, based on CNN image classification applied on a Xilinx Zynq FPGA board using the PYNQ framework. Python was used to develop and train CNN models for image classification, exploiting frameworks such as TensorFlow and Keras. The results demonstrate that Deep Learning (DL) models can be applied on a ZYNQ FPGA board to optimize inference time and highlight Faster R-CNN as the most effective model for image classification, contributing to strengthening the physical security of building access.