Quantized Neural Networks and Neuromorphic Computing for Embedded Systems
Deep learning techniques have made great success in areas such as computer vision, speech recognition and natural language processing. Those breakthroughs made by deep learning techniques are changing every aspect of our lives. However, deep learning techniques have not realized their full potential...
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| Main Authors: | , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
IntechOpen
2020
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| Subjects: | |
| Online Access: | https://www.intechopen.com/chapters/71591 |
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| Summary: | Deep learning techniques have made great success in areas such as computer vision, speech recognition and natural language processing. Those breakthroughs made by deep learning techniques are changing every aspect of our lives. However, deep learning techniques have not realized their full potential in embedded systems such as mobiles, vehicles etc. because the high performance of deep learning techniques comes at the cost of high computation resource and energy consumption. Therefore, it is very challenging to deploy deep learning models in embedded systems because such systems have very limited computation resources and power constraints. Extensive research on deploying deep learning techniques in embedded systems has been conducted and considerable progress has been made. In this book chapter, we are going to introduce two approaches. The first approach is model compression, which is one of the very popular approaches proposed in recent years. Another approach is neuromorphic computing, which is a novel computing system that mimicks the human brain. |
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| ISBN: | 978-1-83880-522-7 |
| DOI: | 10.5772/intechopen.91835 |