Efficient data encoding for convolutional neural network application

H.-P. Trinh; M. Duranton; M. Paindavoine

Efficient data encoding for convolutional neural network application

H.-P. Trinh ¹ M. Duranton ¹ M. Paindavoine ²

1 DACLE-LIST - Département d'Architectures, Conception et Logiciels Embarqués-LIST

2 LEAD - Laboratoire d'Etude de l'Apprentissage et du Développement [Dijon]

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)

Document type :

Journal articles

Domain :

Computer Science [cs]

Engineering Sciences [physics]

Complete list of metadatas

https://hal-cea.archives-ouvertes.fr/cea-01846867
Contributor : Léna Le Roy <>
Submitted on : Monday, July 23, 2018 - 8:01:25 AM
Last modification on : Thursday, March 14, 2019 - 9:46:19 AM

Efficient data encoding for convolutional neural network application

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Efficient data encoding for convolutional neural network application

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