Mobile_ConvNet

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RenderScript based implementation of Convolutional Neural Networks for Android phones

Mobile_ConvNet 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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## Android and RenderScript Based Convolutional Neural Networks
The project implements [SqueezeNet](https://arxiv.org/abs/1602.07360) on Android phones. We use RenderScript efficiently to minimize the execution time. I have tested the project on three different Android phones (Nexus 5, Nexus 6P and Samsung Galaxy). The achieved speedups on these platforms are 310X, 133.89X, and 59.54X respectively. The results are submitted to a conference as a paper which is under review.
## Documentation
As the documentation overlaps with the content of the paper, we will publish them when we get the result of submission. However, if you are interested in working on the project, please contact me for more information.

## Email
mmotamedi@ucdavis.edu
## Citing
If this project helped your research, please kindly cite our work.
```
Motamedi, Mohammad, Daniel Fong, and Soheil Ghiasi. "Cappuccino: Efficient Inference Software Synthesis for Mobile System-on-Chips." arXiv preprint arXiv:1707.02647 (2017).
```

Mobile_ConvNet

Mobile_ConvNet 최신버젼 다운로드

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Mobile_ConvNet 최신버젼 다운로드

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