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About Deep Learning On Mobile Devices-A Review: March 2019
This document provides a review of deep learning on mobile devices. It discusses hardware architectures like FPGAs, ASICs, and mobile GPUs for mobile deep learning. It covers algorithm optimizations like quantization and pruning to simplify computation while maintaining accuracy. It reviews mobile deep learning platforms like TensorFlow, MXNet, MACE, and Paddle-mobile. It discusses applications in areas like robotics, healthcare, and autonomous driving. It also addresses challenges like handling small training datasets.
Detailed Information
| Author: | ['Jorge Luis Morton'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 16 |
| Language: | English |
| Format: | |
| Price: | FREE |
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