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降阶多端口并行和多向神经联想存储器。

Reduced order multiport parallel and multidirectional neural associative memories.

作者信息

Bhatti Abdul Aziz

机构信息

School of Science and Technology, University of Management and Technology, Lahore, Pakistan.

出版信息

Biol Cybern. 2009 May;100(5):395-407. doi: 10.1007/s00422-009-0310-0. Epub 2009 Apr 21.

Abstract

This paper proposes multiport parallel and multidirectional intraconnected associative memories of outer product type with reduced interconnections. Some new reduced order memory architectures such as k-directional and k-port parallel memories are suggested. These architectures are, also, very suitable for implementation of spatio-temporal sequences and multiassociative memories. It is shown that in the proposed memory architectures, a substational reduction in interconnections is achieved if the actual length of original N-bit long vectors is subdivided into k sublengths. Using these sublengths, submemory matrices, T ( s ) or W ( s ), are computed, which are then intraconnected to form k-port parallel or k-directional memories. The subdivisions of N-bit long vectors into k sublengths save ((k-1) x 100) / k % of interconnections. It is shown, by means of an example, that more than 80% reduction in interconnections is achieved. Minimum limit in bits on k as well as maximum limit on subdivisions in k is determined. The topologies of reduced interconnectivity developed in this paper are symmetric in structure and can be used to scale up to larger systems. The underlying principal of construction, storage and retrieval processes of such associative memories has been analyzed. The effect of complexity of different levels of reduced interconnectivity on the quality of retrieval, signal to noise ratio, and storage capacity has been investigated. The model possesses analogies to biological neural structures and digital parallel port memories commonly used in parallel and multiprocessing systems.

摘要

本文提出了具有减少互连的外积型多端口并行和多向内部互连联想存储器。提出了一些新的降阶存储器架构,如k向和k端口并行存储器。这些架构也非常适合实现时空序列和多联想存储器。结果表明,在所提出的存储器架构中,如果将原始N位长向量的实际长度细分为k个子长度,则互连可实现大幅减少。利用这些子长度计算子存储器矩阵T(s)或W(s),然后将它们内部互连以形成k端口并行或k向存储器。将N位长向量细分为k个子长度可节省((k - 1)×100)/k%的互连。通过一个例子表明,互连减少了80%以上。确定了k的最小位数限制以及k的最大细分限制。本文开发的降低互连性的拓扑结构在结构上是对称的,可用于扩展到更大的系统。分析了这种联想存储器的构造、存储和检索过程的基本原理。研究了不同程度的降低互连性的复杂度对检索质量、信噪比和存储容量的影响。该模型类似于生物神经结构和并行及多处理系统中常用的数字并行端口存储器。

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