The Experts below are selected from a list of 3 Experts worldwide ranked by ideXlab platform

Dharmajee Rao D.t.v. - One of the best experts on this subject based on the ideXlab platform.

  • A Novel Approach for Efficient Training of Deep Neural Networks
    Institute of Advanced Engineering and Science, 2018
    Co-Authors: Dharmajee Rao D.t.v., Ramana K.v.
    Abstract:

    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

Ramana K.v. - One of the best experts on this subject based on the ideXlab platform.

  • A Novel Approach for Efficient Training of Deep Neural Networks
    Institute of Advanced Engineering and Science, 2018
    Co-Authors: Dharmajee Rao D.t.v., Ramana K.v.
    Abstract:

    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