An associative memory-based learning model with an efficient hardware implementation in FPGA

Authors:
Ali Ahmadi;Hans Jürgen Mattausch;M. Anwarul Abedin;Mahmoud Saeidi;Tetsushi Koide
Affiliations:
Electrical and Computer College, Khajeh-Nasir University of Technology, Shariati St., Tehran, Iran;Research Institute for Nanodevice & Bio System (RNBS), Hiroshima University, Higashi-Hiroshima, Japan;Department of Electrical & Electronic Engineering, Dhaka University of Engineering & Technology, Bangladesh;Education & Research Institute for Information & Communication Technologies, Tehran, Iran;Research Institute for Nanodevice & Bio System (RNBS), Hiroshima University, Higashi-Hiroshima, Japan
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In this paper we propose a learning model based on a short- and long-term memory and a ranking mechanism which manages the transition of reference vectors between the two memories. Furthermore, an optimization algorithm is used to adjust the reference vectors components as well as their distribution, continuously. Comparing to other learning models like neural networks, the main advantage of the proposed model is that a pre-training phase is unnecessary and it has a hardware-friendly structure which makes it implementable by an efficient LSI architecture without requiring a large amount of resources. A prototype system is implemented on an FPGA platform and tested with real data of handwritten and printed English characters delivering satisfactory classification results.