Open AccessA Novel Approach for Efficient Training of Deep Neural Networks
Author(s) -
D.T.V. Dharmajee Rao,
K. V. V. Ramana
Publication year - 2018
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v11.i3.pp954-961
Subject(s) - artificial neural network , computer science , matrix multiplication , multiplication (music) , backpropagation , speedup , parallel computing , matrix (chemical analysis) , span (engineering) , algorithm , artificial intelligence , arithmetic , mathematics , engineering , physics , materials science , civil engineering , quantum mechanics , combinatorics , composite material , quantum
Deep Neural Network training algorithms consumes long training time, especially when the number of hidden layers and nodes is large. Matrix multiplication is the key operation carried out at every node of each layer for several hundreds of thousands of times during the training of Deep Neural Network. Blocking is a well-proven optimization technique to improve the performance of matrix multiplication. Blocked Matrix multiplication algorithms can easily be parallelized to accelerate the performance further. This paper proposes a novel approach of implementing Parallel Blocked Matrix multiplication algorithms to reduce the long training time. The proposed approach was implemented using a parallel programming model OpenMP with collapse() clause for the multiplication of input and weight matrices of Backpropagation and Boltzmann Machine Algorithms for training Deep Neural Network and tested on multi-core processor system. Experimental results showed that the proposed approach achieved approximately two times speedup than classic algorithms.