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A two-level hamming network for high performance associative memory
1Department of Computer Science & Electronic Engineering, Tokuyama College of Technology, Yamaguchi, Japan. n-ikeda@tokuyama.ac.jp
Abstract:
This paper presents an analysis of a two-level decoupled Hamming network, which is a high performance discrete-time/discrete-state associative memory model. The two-level Hamming memory generalizes the Hamming memory by providing for local Hamming distance computations in the first level and a voting mechanism in the second level. In this paper, we study the effect of system dimension, window size, and noise on the capacity and error correction capability of the two-level Hamming memory. Simulation results are given for both random images and human face images.
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