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.
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1-23 |
| Number of pages | 23 |
| Journal | Neural Processing Letters |
| Volume | 40 |
| Issue number | 1 |
| Early online date | 13 Jun 2013 |
| DOIs | |
| Publication status | Published - 2013 |