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ENAMeL : a language for binary correlation matrix memories: reducing the memory constraints of matrix memories

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JournalNeural Processing Letters
DateE-pub ahead of print - 13 Jun 2013
DatePublished (current) - 2013
Issue number1
Volume40
Number of pages23
Pages (from-to)1-23
Early online date13/06/13
Original languageEnglish

Abstract

Despite their relative simplicity, Correlation Matrix Memories (CMMs) are an
active area of research, as they are able to be integrated into more complex architectures such as the Associative Rule Chaining Architecture (ARCA) [1]. In this architecture, CMMs are used effectively in order to reduce the time complexity of a tree search from O(bd) to O(d)—where b is the branching factor and d is the depth of the tree. This paper introduces the Extended Neural Associative Memory Language (ENAMeL)—a domain specific language developed to ease development of applications using correlation matrix memories (CMMs). We discuss various considerations required while developing the language, and
techniques used to reduce the memory requirements of CMM-based applications. Finally we show that the memory requirements of ARCA when using the ENAMeL interpreter compare favourably to our original results [1] run in MATLAB.

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