Modeling and Analysis of Associative Memory Networks (EIA)

关联存储器由具有数据存储能力的互连元件的集合组成。通过数据探测向量的内容而不是通过特定地址来并行访问元素。最近，被称为Hopfield AMN的模型受到了极大的关注。这些模型易于分析，并展示了神经网络的一些行为特征。为了结合这些特征，将探索一些新的模型，这些模型保留了Hopfield AMN的足够简单的结构，使它们易于分析。目的是为了更好地了解各种类型的AMN的性质和行为。首席研究员正在分析几种新型号的动态和信息存储能力。许多新的模型受到一些物理约束和互联成本的限制，将各种网络设计和分析问题归结为优化问题是合适的。这项研究将对AMN在未来的计算机体系结构中的使用具有重要意义，与信号处理、通信、控制和人工智能方面的挑战性问题相匹配。了解各种AMN的参数与其信息存储能力之间的关系是确定这种形式的并行分布式计算的最有用的方面的一步。

Associative memories are composed of a collection of interconnected elements having data storage capabilites. The elements are accessed in parallel by the content of a data probe vector rather than by a specific address. Recently, models referred to as the Hopfield AMN have received much attention. These models are simple to analyze and exhibit some of the behavioral features of neural networks. To incorporate these features, some new models will be explored which retain enough of the simple structure of the Hopfield AMN that makes them amenable to analysis. The objective is to obtain a better understanding of the properties and behavior of various types of AMN. The principal investigator is analyzing the dynamic and information storage capabilities of several new models. Many of the new models are subject to some physical constraints and interconnection costs, and it is appropriate to formulate various network design and analysis problems as optimization problems. The research will have important implications for the use of AMN in computer architectures of the future, matched to challenging problems in signal processing, communications, control, and artificial intelligence. Understanding the relationship between parameters of various AMN and their information storage capacities is a step towards determining the most useful aspects of this form of parallel distributed computing.