RECONFIGURABLE, HYPERDIMENSIONAL NEURAL NETWORK ARCHITECTURE

Method and apparatus for processing data using a reconfigurable, hyperdimensional neural network architecture comprising a feature extractor and a classifier. The feature extractor comprises a neural network for encoding input information into hyperdimensional (HD) vectors and extracting at least on...

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Bibliographische Detailangaben
Hauptverfasser:	KANDASWAMY, Indhumathi, VAN DER WAL, Gooitzen S, PIACENTINO, Michael R, FARKYA, Saurabh, NADAMUNI RAGHAVAN, Aswin, ZHANG, David Chao, ZHANG, Yuzheng, DANIELS, Zachary, ISNARDI, Michael A
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS
Online-Zugang:	Volltext bestellen
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Beschreibung
Zusammenfassung:	Method and apparatus for processing data using a reconfigurable, hyperdimensional neural network architecture comprising a feature extractor and a classifier. The feature extractor comprises a neural network for encoding input information into hyperdimensional (HD) vectors and extracting at least one particular HD vector representing at least one feature within the input information, wherein the neural network comprises no more than one multiply and accumulate operator. The classifier is coupled to the feature extractor for classifying the at least one particular HD vector to produce an indicium of classification for the at least one particular HD vector and wherein the classifier does not comprise any multiply and accumulate operators.