Optical implementation of large-scale neural networks using a time-division-multiplexing technique
| Autor: | Jun Ohta, Kazuo Kyuma, Masaya Oita, Shuichi Tai |
|---|---|
| Rok vydání: | 1990 |
| Předmět: |
Artificial neural network
business.industry Computer science Optical computing State vector Content-addressable memory Atomic and Molecular Physics and Optics Optics Time-division multiplexing Pattern recognition (psychology) Electronic engineering Time domain Telecommunications business Associative property |
| Zdroj: | Optics Letters. 15:227 |
| ISSN: | 1539-4794 0146-9592 |
| DOI: | 10.1364/ol.15.000227 |
| Popis: | A new architecture for optical implementation of large-scale neural networks is proposed. This architecture is based on a time-division-multiplexing technique, in which both the neuron state vector and the interconnection matrix are divided in the time domain. Computer simulation and experimental results for associative memories show the effectiveness in implementing large-scale networks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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