MathWorks Announces Release 2017b of the MATLAB and Simulink Product Families


NATICK, Mass.–(BUSINESS WIRE)–MathWorks today introduced Release 2017b (R2017b), which includes new features in MATLAB and Simulink, six new products, and updates and bug fixes to 86 other products. The release also adds new important deep learning capabilities that simplify how engineers, researchers, and other domain experts design, train, and deploy models.

Deep Learning Support

Specific deep learning features, products, and capabilities in R2017b include:

  • Neural Network Toolbox has added support for complex architectures, including directed acyclic graph (DAG) and long short-term memory (LSTM) networks, and provides access to popular pretrained models such as GoogLeNet.
  • The Image Labeler app in Computer Vision System Toolbox now provides a convenient and interactive way to label ground truth data in a sequence of images. In addition to object detection workflows, the toolbox now also supports semantic segmentation using deep learning to classify pixel regions in images and to evaluate and visualize segmentation results.
  • A new product, GPU Coder, automatically converts deep learning models to CUDA code for NVIDIA GPUs. Internal benchmarks show the generated code for deep learning inference achieves up to 7x better performance than TensorFlow and 4.5x better performance than Caffe2 for deployed models.*

Together with capabilities introduced in R2017a, pretrained models can be used for transfer learning, including convolutional neural networks (CNN) models (AlexNet, VGG-16, and VGG-19), as well as models from Caffe (including Caffe Model Zoo). Models can be developed from scratch, including using CNNs for image classification, object detection, regression, and more.

“With the growth of smart devices and IOT, design teams face the challenge of creating more intelligent products and applications by either developing deep learning skills themselves, or relying on other teams with deep learning expertise who may not understand the application context,” said David Rich, MATLAB marketing director, MathWorks. “With R2017b, engineering and system integration teams can extend the use of MATLAB for deep learning to better maintain control of the entire design process and achieve higher-quality designs faster. They can use pretrained networks, collaborate on code and models, and deploy to GPUs and embedded devices. Using MATLAB can improve result quality while reducing model development time by automating ground truth labeling.”

Additional Updates

In addition to deep learning, R2017b also includes a series of updates in other key areas, including:

  • Data Analytics with MATLAB
    • A new Text Analytics Toolbox product, extensible datastore, more big data plots and algorithms for machine learning, and Microsoft Azure blob storage support
  • Real-Time Software Modeling with Simulink
    • Model scheduling effects and implement pluggable components for software environments
  • Verification and Validation with Simulink
    • New tools for requirements modeling, test coverage analysis, and compliance checking

R2017b is available immediately worldwide. For more details on the complete list of updates, please visit the new release page.

About MathWorks

MathWorks is the leading developer of mathematical computing software. MATLAB, the language of technical computing, is a programming environment for algorithm development, data analysis, visualization, and numeric computation. Simulink is a graphical environment for simulation and Model-Based Design for multidomain dynamic and embedded systems. Engineers and scientists worldwide rely on these product families to accelerate the pace of discovery, innovation, and development in automotive, aerospace, electronics, financial services, biotech-pharmaceutical, and other industries. MATLAB and Simulink are also fundamental teaching and research tools in the world’s universities and learning institutions. Founded in 1984, MathWorks employs more than 3500 people in 15 countries, with headquarters in Natick, Massachusetts, USA. For additional information, visit

* Internal benchmarks were performed for inference performance of AlexNet using a TitanXP GPU and Intel(R) Xeon(R) CPU E5-1650 v4 @ 3.60GHz. Software versions used were MATLAB(R2017b), TensorFlow(1.2.0), and Caffe2(0.8.1). The GPU accelerated versions of each software were used for benchmarks. All tests were run on Windows 10.

MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.

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