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dc.contributor.authorTuo-Hung HOUen_US
dc.contributor.authorChih-Cheng CHANGen_US
dc.contributor.authorJen-Chieh LIUen_US
dc.date.accessioned2019-04-11T06:09:18Z-
dc.date.available2019-04-11T06:09:18Z-
dc.date.issued2018-11-15en_US
dc.identifier.govdocG06N003/08en_US
dc.identifier.govdocG06N007/00en_US
dc.identifier.govdocG06N003/04en_US
dc.identifier.urihttp://hdl.handle.net/11536/151450-
dc.description.abstractA neural network processing system includes at least one synapse and a neuron circuit. The synapse receives an input signal and has an external weighted value and an internal weighted value, and the internal weighted value has a variation caused by an external stimulus. When the variation of the internal weighted value accumulates to a threshold value, the external weighted value varies and the input signal is multiplied by the external weighted value of the synapse to generate a weighted signal. A neuron circuit is connected with the synapse to receive the weighted signal transmitted by the synapse, and calculates and outputs the weighted signal. The present invention can simultaneously accelerate the prediction and learning functions of the deep learning and realize a hardware neural network with high precision and real-time learning.en_US
dc.language.isoen_USen_US
dc.titleNEURAL NETWORK PROCESSING SYSTEMen_US
dc.typePatentsen_US
dc.citation.patentcountryUSAen_US
dc.citation.patentnumber20180330236en_US
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