Approximate Deep Neural Networks for Embedded Platforms

Abstract

Deep Neural networks are among the most powerful machine learning techniques that are becoming very interesting for Big Data applications. In the context of embedded platforms, implementing efficient DNNs in terms of performance and energy consumption while they maintain a required quality is very challenging. Sparsity can be used as an intelligent technique for reducing the size of DNNs. The purpose of this research is to explore the possibilities of introducing sparsity to CNNs and to evaluate their performance.