Implementation of Deep Learning models on an FPGA board for voice classification

This paper presents an implementation of an intelligent door security system, based on voice classification of people requesting access to datacenter rooms, by applying deep learning models (CNN and RNN) implemented on an FPGA board. Our approach is to use the ZYBO Xilinx-Zynq7000 FPGA board, which integrates the PYNQ framework and enables the use of the PetaLinux operating system, to facilitate the deployment of our deep learning models on the FPGA board. We also use deep learning frameworks such as TensorFlow and Keras for model development and training. We add the MFCC (Mel Frequency Cepstrum Coefficient) layer to extract parameters features to describe the voice in form of data. The results of this work demonstrate that the ZYNQ FPGA board is well suited to parallel computation of deep learning models (CNN and RNN) for real-time applications, with the highest accuracy.