Multilayer Perceptron with Backpropagation, HDL Coder, and FPGA Technology: An Integrated Approach for Efficient Neural Network Implementation

Abstract

Multilayer perceptron (MLP) and the backpropagation method are two fundamental components in the field of artificial neural networks (ANNs) that have gained significant attention and applications in various areas of computer science and machine learning. This article provides an overview of MLP and the backpropagation method, discussing their underlying principles and functioning. Initially, we provide a general overview of MLP and its architecture. MLP is a form of feedforward neural networks that includes one or more hidden layers between the input and output layers. Each neuron computation in MLP is performed in a forward pass, applying a nonlinear activation function to the weighted sum of inputs and neuron activations from the previous layer. Next, the backpropagation method, which is used for training MLP, is discussed. This method primarily relies on minimizing the error between the predicted and true outputs of the network using gradient descent. The error gradient is propagated backward through the network, updating the weights of each connection, contributing to its effectiveness and learning capability. Various alternative variations and enhancements of MLP and the backpropagation method are then explored, including the use of different activation functions, regularization, architectural modifications, and optimization methods. Significant research has been conducted in recent years to develop more efficient and powerful MLP models. In conclusion, we summarize the importance and applications of MLP and the backpropagation method. MLP and backpropagation are widely used tools for tasks such as classification, regression, function approximation, and others, and they continue to be the subject of active research and development in the field of machine learning and neural networks.