Analysis and Design of High Performance Deep Learning Algorithm: Convolutional Neural Networks
How to Cite?
Sunil Pandey, Naresh Kumar Nagwani, Shrish Verma, "Analysis and Design of High Performance Deep Learning Algorithm: Convolutional Neural Networks," International Journal of Engineering Trends and Technology, vol. 69, no. 6, pp. 216-224, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I6P231
Deep learning algorithms like convolutional neural networks (CNNs) have a multi-layered computational design. The CNN comprises of stacks of different layers which perform feature engineering and training or classification computations on the inputs which are generally 3-D tensor datasets. Training a CNN is very demanding in terms of computational resources and time. Training times of several weeks and even months are not unheard of. This is one of the important reasons limiting widespread adoption of CNNs in new applications. Performance enhancement of CNNs is therefore an active R&D area. In view of this, the design of CNN algorithms for high performance distributed and parallel computing architectures assumes significance. The CNN can be conceptualized as a pipeline system which makes CNNs amenable to pipeline parallelism. In the present work, a pipeline computation design and model of the CNN has been proposed. The performance of the pipeline model of the CNN has been analyzed based on representative data generated through different computational experiments. Analysis shows that a net performance gain of 18X can be achieved on a CNN feature engineering pipeline by combining pipeline parallelism with task parallelism.
Deep Learning, Convolutional Neural Networks, Pipeline Computing, Pipeline Parallelism, Task Parallelism, High Performance Computing.
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