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Low Temperature Performance of High-Speed Neural Network Circuits

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dc.contributor.author Duong, T. en_US
dc.contributor.author Tran, M. en_US
dc.contributor.author Daud, T. en_US
dc.contributor.author Thakoor, A. en_US
dc.date.accessioned 2004-10-05T05:52:28Z
dc.date.available 2004-10-05T05:52:28Z
dc.date.issued 1995-05-21 en_US
dc.identifier.citation Reno, Nevada, USA en_US
dc.identifier.clearanceno 94-1531 en_US
dc.identifier.uri http://hdl.handle.net/2014/33364
dc.description.abstract Artificial neural networks, derived from their biological counterparts, offer a new and enabling computing paradigm specially suitable for such tasks as image and signal processing with feature classification/object recognition, global optimization, and adaptive control. When implemented in fully parallel electronic hardware, it offers orders of magnitude speed advantage. Basic building blocks of the new architecture are the processing elements called neurons implemented as nonlinear operational amplifiers with sigmoidal transfer function, interconnected through weighted connections called synapses implemented using circuitry for weight storage and multiply functions either in an analog, digital, or hybrid scheme. en_US
dc.format.extent 135562 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject.other artificial neural networks image processing signal processing en_US
dc.title Low Temperature Performance of High-Speed Neural Network Circuits en_US


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