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Journal articles
Efficient data encoding for convolutional neural network application
Abstract : This article presents an approximate data encoding scheme called Significant Position Encoding (SPE). The encoding allows efficient implementation of the recall phase (forward propagation pass) of Convolutional Neural Networks (CNN)-a typical Feed-Forward Neural Network.This implementation uses only7bits data representation and achieves almost the same classification performance compared with the initial network: on MNIST handwriting recognition task, using this data encoding scheme losses only 0.03% in terms of recognition rate (99.27% vs. 99.3%). In terms of storage, we achieve a 12.5% gain compared with an 8 bits fixed-point implementation of the same CNN. Moreover, this data encoding allows efficient implementation of processing unit thanks to the simplicity of scalar product operation-the principal operation in a Feed-Forward Neural Network.
Keywords :
Character recognition
Convolution
Digital storage
Feedforward neural networks
Neural networks
Canonical signed digits
Classification performance
Convolutional neural network
Data representations
Efficient implementation
Fixed-point implementation
Forward propagation
Handwriting recognition
Encoding (symbols)
https://hal-cea.archives-ouvertes.fr/cea-01846867
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Monday, July 23, 2018 - 8:01:25 AM
Last modification on : Friday, June 25, 2021 - 9:48:10 AM
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Monday, July 23, 2018 - 8:01:25 AM
Last modification on : Friday, June 25, 2021 - 9:48:10 AM
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