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Oscillator Array Models for Associative Memory and Pattern Recognition
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DOI：10.1109/TCSI.2015.2418851.png)
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Abstract
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Brain-inspired arrays of parallel processing oscillators represent an intriguing alternative to traditional computational methods for data analysis and recognition. This alternative is now becoming more concrete thanks to the advent of emerging oscillators fabrication technologies providing high density packaging and low power consumption. One challenging issue related to oscillator arrays is the large number of system parameters and the lack of efficient computational techniques for array simulation and performance verification. This paper provides a realistic phase-domain modeling and simulation methodology of oscillator arrays which is able to account for the relevant device non-idealities. The model is employed to investigate the associative memory performance of arrays composed of resonant LC oscillators.
Keywords:
Associative memory
neurocomputing
oscillator array
phase-domain modeling
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