Deploying a Deep Neural Network on FPGA

This chapter presents the deployment of a deep neural network (DNN) on an FPGA using MATLAB, Simulink, and Vivado. The chapter begins with preprocessing the diabetes dataset from Kaggle and training a neural network model in MATLAB using feedforward architecture and backpropagation. Activation functions such as sigmoid, tanh, and ReLU are approximated using piecewise linear functions for hardware compatibility. The trained model is converted to fixed‐point representation and implemented in Simulink using HDL coder to generate synthesizable VHDL code. The VHDL design is integrated into Vivado for synthesis, simulation, and verification. Accuracy comparison between MATLAB simulation and FPGA implementation demonstrates the practical feasibility of deploying DNNs on hardware platforms for predictive healthcare applications.