MathWorks - Mobile View
  • 碻形冰暨硞 MathWorks 帐憷碻形冰暨硞 MathWorks 帐憷
  • Access your MathWorks Account
    • 我的帐户
    • 我的社区资料
    • 关联许可证
    • 登出
  • 产品
  • 解决方案
  • 学术
  • 支持
  • 社区
  • 活动
  • 获取 MATLAB
MathWorks
  • 产品
  • 解决方案
  • 学术
  • 支持
  • 社区
  • 活动
  • 获取 MATLAB
  • 碻形冰暨硞 MathWorks 帐憷碻形冰暨硞 MathWorks 帐憷
  • Access your MathWorks Account
    • 我的帐户
    • 我的社区资料
    • 关联许可证
    • 登出

视频与网上研讨会

  • MathWorks
  • 视频
  • 视频首页
  • 搜索
  • 视频首页
  • 搜索
  • 联系销售
  • 试用软件
  Register to watch video
  • Description
  • Full Transcript
  • Related Resources

Applied Machine Learning, Part 4: Embedded Systems

From the series: Applied Machine Learning

Seth Deland, MathWorks

Walk through several key techniques and best practices for running your machine learning model on embedded devices. 

The video discusses options for making your model faster and reducing its memory footprint, including automatic C/C++ code generation, feature selection, and model reduction.

The phrase “machine learning” brings to mind complex algorithms that use lots of computations to train a model. But computations on “embedded devices” are limited in the amount of memory and compute available. 

Now, when I say “embedded devices,” I’m referring to objects with a special-purpose computing system, so think of things like a household appliance or sensors in an autonomous vehicle.

Today, we’ll discuss the different factors to keep in mind when preparing your machine learning model for an embedded device. 

Different types of models require different amounts of memory and time in order to make a prediction. For example, single decision trees are fast and require a small amount of memory. Nearest neighbor methods are slower and require more memory, so you might not want to use them for embedded applications.

Another thing to keep in mind when determining which models to use on an embedded device is how you will get your model to the device.  

Most embedded systems are programmed in low-level languages such as C.

But machine learning is typically done in high-level interpreted languages such as MATLAB, Python, or R.

If you have to maintain code bases in 2 different languages, it is going to be very painful to keep them in sync.   

MATLAB provides tools that automatically convert a machine learning model to C code, so you don’t need to manually implement the model in C separately.

So what if, after converting a model to C, you find out that it isn’t going to meet the requirements of our system?  Maybe the memory footprint is too big, or the model takes too long to make predictions?  

You could try other types of models and see if the code meets the requirements.  Maybe start with a simple model such as a decision tree.  

Alternatively, you could go back earlier in the process and see if you can reduce the number of features in the model. You can use tools such as neighborhood component analysis, which are useful for determining the impact that the features have on the results.  If you see that some features are weighted low, you could drop them from our model, making our model more concise.

Certain types of models have different reduction techniques associated with them. For decision trees, you can use pruning techniques, where you drop nodes that provide the smallest accuracy improvement.

One other approach is to look at reducing the memory required for storing the model parameters. For example, seeing if the model can be converted to a fixed-point representation that maintains acceptable accuracy.

Depending on your use case, any of these tactics may be appropriate. Hardware considerations, network connections, and budget are all key factors that will influence design decisions.  

That was just a quick overview of embedding machine learning models. For more information on preparing models for embedded devices, see the links below.

Learn More

Generate C Code for a Machine Learning Model
Use Machine Learning Models in Simulink
Prune a Decision Tree
Related Information
MATLAB for Machine Learning

Feedback

Related Videos:

43:19
Using Machine Learning to Model Complex Systems
30:08
Verification and Validation of Embedded Software Systems
19:50
Requirements Modeling and Design Verification of Embedded...
28:30
Embedded Code Generation for Your Vehicle Control Systems
39:33
Automatic Code Generation for Embedded Control Systems
MathWorks - Domain Selector

Select a Web Site

Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .

Select web site

You can also select a web site from the following list:

How to Get Best Site Performance

Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.

Americas

  • América Latina (Español)
  • Canada (English)
  • United States (English)

Europe

  • Belgium (English)
  • Denmark (English)
  • Deutschland (Deutsch)
  • España (Español)
  • Finland (English)
  • France (Français)
  • Ireland (English)
  • Italia (Italiano)
  • Luxembourg (English)
  • Netherlands (English)
  • Norway (English)
  • Österreich (Deutsch)
  • Portugal (English)
  • Sweden (English)
  • Switzerland
    • Deutsch
    • English
    • Français
  • United Kingdom (English)

Asia Pacific

  • Australia (English)
  • India (English)
  • New Zealand (English)
  • 中国
    • 简体中文Chinese
    • English
  • 日本Japanese (日本語)
  • 한국Korean (한국어)

Contact your local office

  • 联系销售
  • 试用软件

了解产品

  • MATLAB
  • Simulink
  • 学生版软件
  • 硬件支持
  • 文件交换

试用或购买

  • 下载
  • 试用软件
  • 联系销售
  • 定价和许可
  • 如何购买

如何使用

  • 文档
  • 教程
  • 示例
  • 视频与网上研讨会
  • 培训

获取支持

  • 安装帮助
  • MATLAB 问答社区
  • 咨询
  • 许可中心
  • 联系支持

关于 MathWorks

  • 招聘
  • 新闻室
  • 社会愿景
  • 联系销售
  • 关于 MathWorks

MathWorks

Accelerating the pace of engineering and science

MathWorks 公司是世界领先的为工程师和科学家提供数学计算软件的开发商。

发现…

  • Select a Web Site United States
  • 专利
  • 商标
  • 隐私权政策
  • 防盗版
  • 应用状态

京ICP备12052471号

© 1994-2021 The MathWorks, Inc.

  • Facebook
  • Twitter
  • Weibo
  • WeChat

    WeChat

  • LinkedIn
  • RSS

关注我们