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A Mixed Analog-Digital Artificial Neural Network Architecture with On-Chip Learning (1998)
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BibTeX
@MISC{Schmid98amixed,
author = {Alexandre Schmid and Re Schmid and Yusuf Leblebici and Daniel Mlynek},
title = {A Mixed Analog-Digital Artificial Neural Network Architecture with On-Chip Learning},
year = {1998}
}
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Abstract
This paper presents a novel artificial neural network architecture with on-chip learning capability. The issue of straightforward design-flow integration of an autonomous unit is addressed with a mixed analog-digital approach, by implementing a charge-based artificial neural network which interacts with digital control and processing units. We demonstrate the circuit architecture and designflow approach for the case of a Hamming network performing pixel-pattern recognition. Keywords--- Charge-based ANN, mixed-mode ANN hardware architecture, ANN integration design-flow. I. Introduction T HE ABILITY of artificial neural networks (ANN) to acquire knowledge of their surrounding environment and adapt to it, as well as their use of a high degree of computing parallelism makes them very efficient in many application fields including process and quality control, consumer products, optical character and speech recognition, and complex forecasting tasks among many others [1]. Silicon implemen...
Keyphrases
on-chip learning speech recognition straightforward design-flow integration hamming network quality control mixed analog-digital approach digital control mixed-mode ann hardware architecture novel artificial neural network architecture optical character many application field high degree on-chip learning capability many others artificial neural network autonomous unit ann integration design-flow consumer product keywords charge-based ann silicon implemen charge-based artificial neural network pixel-pattern recognition surrounding environment complex forecasting task circuit architecture
