Improving storage of patterns in recurrent neural networks: Clone-based model and architecture

Abstract

Artificial neural networks are used in various domains like computer science and computer engineering for tasks like image processing or design of associative memories. The goal is to mimic the impressive brain ability to process or to memorize and retrieve information. Recently a new model of neural network has been proposed and can be used to design associative memories. When considering patterns that are uniformly distributed, this model outperforms existing models like Hopfield Networks. However, when considering non-uniformly distributed patterns, its performance highly degrades. Few propositions have been made to address this problem. However, they require designing complex hardware architectures to be efficient. In this paper, we propose a new binary neural network model that allows reaching good performances at low hardware cost.