Hardware implementation of Hopfield-like neural networks: Quantitative analysis of FPGA approach

Abstract

This paper provides a rigorous analysis of FPGA implementation of Hopfield-like neural networks. The relationship between the hardware resources used to synthesize the data path and those used to provide network connections is discussed, as well as the distribution of these resources and how it depends on the variation in the architectural parameters of the network. The analysis presented in this paper is based on Intel/Altera Cyclone FPGA devices.