An Energy Efficient Additive Neural Network

Date

2017-05-18

Author

Afrasiyabi, Arman
Nasır, Barış
Yildiz, Ozan
Yarman Vural, Fatoş Tunay
ÇETİN, AHMET ENİS

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this paper, we propose a new energy efficient neural network with the universal approximation property over space of Lebesgue integrable functions. This network, called additive neural network, is very suitable for mobile computing. The neural structure is based on a novel vector product definition, called ef-operator, that permits a multiplier-free implementation. In ef-operation, the "product" of two real numbers is defined as the sum of their absolute values, with the sign determined by the sign of the product of the numbers. This "product" is used to construct a vector product in n-dimensional Euclidean space. The vector product induces the lasso norm. The proposed additive neural network successfully solves the XOR problem. The experiments on MNIST dataset show that the classification performances of the proposed additive neural networks are very similar to the corresponding multi-layer perceptron.

Subject Keywords

Energy efficient, Efficient ANN, Neural network, Machine learning, Multiplierless ann, Mnist, Xor

URI

https://hdl.handle.net/11511/53149

Conference Name

25th Signal Processing and Communications Applications Conference (SIU)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

A. Afrasiyabi, B. Nasır, O. Yildiz, F. T. Yarman Vural, and A. E. ÇETİN, “An Energy Efficient Additive Neural Network,” presented at the 25th Signal Processing and Communications Applications Conference (SIU), Antalya, Turkey, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53149.