Title :
Multiplication-free Neural Networks
Author :
Akbas, Cem Emre ; Bozkurt, Alican ; Cetin, A. Enis ; Cetin-Atalay, Rengul ; Uner, Aysegul
Author_Institution :
Bilkent Univ. Elektrik ve Elektron. Muhendisligi Bolumu, Ankara, Turkey
Abstract :
In this article, a multiplication-free artificial Neural Network (ANN) structure is proposed. Inner products between the input vectors and the ANN weights are implemented using a multiplication-free vector operator. Training of the new artificial neural network structure is carried out using the sign-LMS algorithm. Proposed ANN system can be used in applications requiring low-power usage or running on microprocessors that have limited processing power.
Keywords :
least mean squares methods; neural nets; vectors; ANN structure; ANN weights; input vector; multiplication-free artificial neural network; multiplication-free vector operator; sign-LMS algorithm; Artificial neural networks; Computational modeling; Conferences; Convolution; Histograms; Pattern recognition; Artificial Neural Network; Multiplication-free Operator;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2015 23th
Conference_Location :
Malatya
DOI :
10.1109/SIU.2015.7130369
