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| <h1 class="post-title">Deep Learning at the Edge</h1> |
| <h3></h3></header> |
| |
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| <h3 style="text-transform: capitalize; padding-left:10px">faq</h3> |
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| |
| <h1 id="deep-learning-in-a-single-file-for-smart-devices">Deep Learning in a Single File for Smart Devices</h1> |
| |
| <p>Deep learning (DL) systems are complex and often depend on a number of libraries. |
| Porting a DL library to different platforms can be painful, especially for smart devices. |
| One simple solution to this problem is to provide a light interface to the library, complete with all required code in a single file with minimal dependencies. |
| In this document, we explain how to amalgamate all necessary code into a single file, |
| and demonstrate the approach with an example in which we run image recognition on a mobile device.</p> |
| |
| <h2 id="amalgamation-making-the-whole-system-a-single-file">Amalgamation: Making the Whole System a Single File</h2> |
| |
| <p>We come to the idea of of amalgamation following the example of SQLite, |
| which pack all the code needed to run a simple database into a single source file. |
| All that's necessary to create the library is to compile that single file. |
| This simplifies the problem of porting to various platforms.</p> |
| |
| <p>Thanks to <a href="https://github.com/jdeng">Jack Deng</a>, |
| MXNet provides an <a href="https://github.com/apache/mxnet/tree/v1.x/amalgamation">amalgamation</a> script |
| that compiles all code needed for prediction based on trained DL models into a single <code>.cc</code> file, |
| containing approximately 30K lines of code. This code only depends on the BLAS library. |
| Moreover, we've also created an even more minimal version, |
| with the BLAS dependency removed. |
| You can compile the single file into JavaScript by using <a href="https://github.com/kripken/emscripten">emscripten</a>.</p> |
| |
| <p>The compiled library can be used by any other programming language. |
| The <code>.h</code> file contains a light prediction API. |
| Porting to another language with a C foreign function interface requires little effort.</p> |
| |
| <p>For examples, see the following examples on GitHub:</p> |
| |
| <ul> |
| <li>Go: <a href="https://github.com/jdeng/gomxnet">https://github.com/jdeng/gomxnet</a></li> |
| <li>Java: <a href="https://github.com/apache/mxnet/tree/v1.x/amalgamation/jni">https://github.com/apache/mxnet/tree/v1.x/amalgamation/jni</a></li> |
| <li>Python: <a href="https://github.com/apache/mxnet/tree/v1.x/amalgamation/python">https://github.com/apache/mxnet/tree/v1.x/amalgamation/python</a></li> |
| </ul> |
| |
| <p>If you plan to amalgamate your system, there are a few guidelines you ought to observe when building the project:</p> |
| |
| <ul> |
| <li>Minimize dependence on other libraries.</li> |
| <li>Use namespace to encapsulate the types and operators.</li> |
| <li>Avoid running commands such as <code>using namespace xyz</code> on the global scope.</li> |
| <li>Avoid cyclic include dependencies.</li> |
| </ul> |
| |
| <h2 id="image-recognition-demo-on-mobile-devices">Image Recognition Demo on Mobile Devices</h2> |
| |
| <p>With amalgamation, deploying the system on smart devices (such as Android or iOS) is simple. But there are two additional considerations:</p> |
| |
| <ul> |
| <li>The model should be small enough to fit into the device's memory.</li> |
| <li>The model shouldn't be too expensive to run given the relatively low computational power of these devices.</li> |
| </ul> |
| |
| <p>Let's use image recognition as an example. |
| We start with the state-of-the-art inception model. |
| We train it on an ImageNet dataset, |
| using multiple servers with GTX 980 cards. |
| The resulting model fits into memory, |
| but it's too expensive to run. |
| We remove some layers, but now the results are poor.</p> |
| |
| <p>Finally, we show an Android example, thanks to Leliana, <a href="https://github.com/Leliana/WhatsThis">https://github.com/Leliana/WhatsThis</a> to demonstrate how to run on Android.</p> |
| |
| <p><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/apk/subinception.png" height="488" width="274"></p> |
| |
| <p>By using amalgamation, we can easily port the prediction library to mobile devices, with nearly no dependency. |
| After compiling the library for smart platforms, the last thing we must do is to call C-API in the target language (Java/Swift).</p> |
| |
| <p><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/apk/poolnet.png" height="488" width="274"></p> |
| |
| <p>Besides this pre-trained Inception-BatchNorm network, we've provided two pre-trained models.</p> |
| |
| <p>We tested our model on a Nexus 5:</p> |
| |
| <table><thead> |
| <tr> |
| <th></th> |
| <th>Top-1 Validation on ILSVRC2012</th> |
| <th>Time</th> |
| <th>App Size</th> |
| <th>Runtime Temp Memory Req</th> |
| </tr> |
| </thead><tbody> |
| <tr> |
| <td>FastPoorNet</td> |
| <td>around 52%, similar to 2011 winner</td> |
| <td>1s</td> |
| <td><10MB</td> |
| <td><5MB</td> |
| </tr> |
| <tr> |
| <td>Sub InceptionBN</td> |
| <td>around 64%, similar to 2013 winner</td> |
| <td>2.7s</td> |
| <td><40MB</td> |
| <td><10MB</td> |
| </tr> |
| <tr> |
| <td>InceptionBN</td> |
| <td>around 70%</td> |
| <td>4s-5s</td> |
| <td><60MB</td> |
| <td>10MB</td> |
| </tr> |
| </tbody></table> |
| |
| <p>These models are for demonstration only. |
| They aren't fine-tuned for mobile devices, |
| and there is definitely room for improvement. |
| We believe that making a lightweight, portable, |
| and fast deep learning library is fun and interesting, |
| and hope you enjoy using the library.</p> |
| |
| <h2 id="source-code">Source Code</h2> |
| |
| <p><a href="https://github.com/Leliana/WhatsThis">https://github.com/Leliana/WhatsThis</a></p> |
| |
| <h2 id="demo-apk-download">Demo APK Download</h2> |
| |
| <ul> |
| <li><p><a href="https://github.com/dmlc/web-data/blob/master/mxnet/apk/fastpoornet.apk?raw=true">FastPoorNet</a></p></li> |
| <li><p><a href="https://github.com/dmlc/web-data/blob/master/mxnet/apk/subinception.apk?raw=true">SubInception</a></p></li> |
| </ul> |
| |
| </div> |
| </div> |
| |
| </div> |
| </div> |
| |
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