Parallelization of a Neural Network Algorithm for Handwriting Recognition: Can we Increase the Speed, Keeping the Same Accuracy
Date Issued
2021
Author(s)
Todorov, D.
Abstract
This paper examines the problem of parallelizing
neural network training. For our solution we use the backpropagation neural network, as a breakthrough example in the
field of deep learning. The challenge of our solution is to twist
the algorithm in such a way so it can be executed in parallel,
rather than sequentially. In this paper we would like to test
validity of a research hypothesis if we can increase the speed
by parallelizing the back-propagation algorithm and keep the
same accuracy. For this purpose we will develop a use-case of a
handwriting recognition algorithm and run several experiments
to test the performance, both in execution speed and accuracy.
At the end we are going to examine just how much it benefits us
to try and write a parallel program for a neural network, with
regards to the time it takes to train the neural network and the
accuracy of the predictions. Our handwriting problem is that of
classification, and in order to implement any sort of solution, we
must have data. The MNIST dataset of handwritten digits will
provide our necessary data to solve the problem.
neural network training. For our solution we use the backpropagation neural network, as a breakthrough example in the
field of deep learning. The challenge of our solution is to twist
the algorithm in such a way so it can be executed in parallel,
rather than sequentially. In this paper we would like to test
validity of a research hypothesis if we can increase the speed
by parallelizing the back-propagation algorithm and keep the
same accuracy. For this purpose we will develop a use-case of a
handwriting recognition algorithm and run several experiments
to test the performance, both in execution speed and accuracy.
At the end we are going to examine just how much it benefits us
to try and write a parallel program for a neural network, with
regards to the time it takes to train the neural network and the
accuracy of the predictions. Our handwriting problem is that of
classification, and in order to implement any sort of solution, we
must have data. The MNIST dataset of handwritten digits will
provide our necessary data to solve the problem.
Subjects
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