Development and Implementation of Parameterized FPGA-Based General Purpose Neural Networks for Online Applications

Abstract

This paper presents the development and implementation of a generalized backpropagation multilayer perceptron (MLP) architecture described in VLSI hardware description language (VHDL). The development of hardware platforms has been complicated by the high hardware cost and quantity of the arithmetic operations required in online artificial neural networks (ANNs), i.e., general purpose ANNs with learning capability. Besides, there remains a dearth of hardware platforms for design space exploration, fast prototyping, and testing of these networks. Our general purpose architecture seeks to fill that gap and at the same time serve as a tool to gain a better understanding of issues unique to ANNs implemented in hardware, particularly using field programmable gate array (FPGA). The challenge is thus to find an architecture that minimizes hardware costs, while maximizing performance, accuracy, and parameterization. This work describes a platform that offers a high degree of parameterization, while maintaining generalized network design with performance comparable to other hardware-based MLP implementations. Application of the hardware implementation of ANN with backpropagation learning algorithm for a realistic application is also presented.