MIXED-PRECISION DEEP-LEARNING WITH MULTI-MEMRISTIVE DEVICES

A computer-implemented method of mixed-precision deep learning with multi-memristive synapses may be provided. The method comprises representing, each synapse of an artificial neural network by a combination of a plurality of memristive devices, wherein each of the plurality of memristive devices of...

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Bibliographische Detailangaben
Hauptverfasser:	Le Gallo-Bourdeau, Manuel, Boybat Kara, Irem, Sasidharan Rajalekshmi, Nandakumar, Eleftheriou, Evangelos Stavros, Sebastian, Abu
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS
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Beschreibung
Zusammenfassung:	A computer-implemented method of mixed-precision deep learning with multi-memristive synapses may be provided. The method comprises representing, each synapse of an artificial neural network by a combination of a plurality of memristive devices, wherein each of the plurality of memristive devices of each of the synapses contributes to an overall synaptic weight with a related device significance, accumulating a weight gradient ΔW for each synapse in a high-precision variable, and performing a weight update to one of the synapses using an arbitration scheme for selecting a respective memristive device, according to which a threshold value related to the high-precision variable for performing the weight update is set according to the device significance of the respective memristive device selected by the arbitration schema.