System and method for training neural networks with errors

A computing device includes one or more processors, random access memory (RAM), and a non-transitory computer-readable storage medium storing instructions for execution by the one or more processors. The computing device receives first data on which to train a neural network comprising at least one...

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Bibliographic Details
Main Author Tzoufras, Michail
Format Patent
LanguageEnglish
Published 07.02.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
INFORMATION STORAGE
PHYSICS
STATIC STORES
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Summary:A computing device includes one or more processors, random access memory (RAM), and a non-transitory computer-readable storage medium storing instructions for execution by the one or more processors. The computing device receives first data on which to train a neural network comprising at least one quantized layer and performs a set of training iterations to train weights for the neural network. Each training iteration of the set of training iterations includes stochastically writing values to the random access memory for a set of activations of the at least one quantized layer of the neural network using first write parameters corresponding to a first write error rate. The computing device stores trained values for the weights of the neural network. The trained neural network is configured to classify second data based on the stored values.
Bibliography:Application Number: US201916367078