Computational complexity reduction of deep neural networks
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Authors
Im, Mee Seong
Dasari, Venkat R.
Issue Date
2022
Type
Reports
Language
Keywords
Multilayer models , Machine Learning , Mathematica Militaris , Neural Networks , Computational Complexity , Computation reduction
Alternative Title
Abstract
Deep neural networks (DNN) have been widely used and play a major role in the field of computer vision and autonomous navigation. However, these DNNs are computationally complex and their deployment over resource-constrained platforms is difficult without additional optimizations and customization.
In this manuscript, we describe an overview of DNN architecture and propose methods to reduce computational complexity in order to accelerate training and inference speeds to fit them on edge computing platforms with low computational resources.
Description
Citation
Mee Seong Im, Venkat Dasari. "Computational complexity reduction of deep neural networks". Mathematica Militaris, Volume 25. 2022.
Publisher
United States Service Academies