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GPU Coder

Generate CUDA code for NVIDIA GPUs

GPU Coder™ generates optimized CUDA® code from MATLAB® code for deep learning, embedded vision, and autonomous systems. The generated code calls optimized NVIDIA® CUDA libraries, including cuDNN, cuSolver, and cuBLAS. It can be integrated into your project as source code, static libraries, or dynamic libraries, and can be used for prototyping on GPUs such as the NVIDIA Tesla® and NVIDIA Tegra®. You can use the generated CUDA within MATLAB to accelerate computationally intensive portions of your MATLAB code. GPU Coder lets you incorporate legacy CUDA code into your MATLAB algorithms and the generated code.

When used with Embedded Coder®, GPU Coder lets you verify the numerical behavior of the generated code via software-in-the-loop (SIL) testing.

Key Features

  • CUDA C and C++ code generation

  • Deep learning network support (with Neural Network Toolbox™)

  • Image processing support (Image Processing Toolbox™)

  • Loop optimizations and CUDA kernel optimizations

  • MEX function generation for code verification and acceleration

  • Legacy CUDA code integration

  • Code profiling and verification

LTE HDL Toolbox

Model LTE communications subsystems for FPGAs and ASICs

LTE HDL Toolbox™ provides sample-based algorithms in Simulink® blocks for the design and implementation of LTE wireless communications subsystems on FPGAs and ASICs. Toolbox algorithms, gateways between frame-based and sample-based processing, and reference applications enable you to compose an LTE baseband communications subsystem in Simulink.

You can modify the reference applications for integration into your own design. HDL implementations of the toolbox algorithms are optimized for efficient resource usage and performance for prototyping or production deployment on FPGA and ASIC devices.

The toolbox algorithms are designed to generate readable, synthesizable code in VHDL® and Verilog® (with HDL Coder™). For over-the-air testing of LTE designs, you can connect transmitter and receiver models to radio devices (with Communications System Toolbox™ hardware support packages).

Key Features

  • Standard-compliant LTE Simulink blocks, including encoders and decoders such as turbo, convolutional, and CRC

  • Ready-to-implement reference applications, including signal detection (PSS and SSS) and decoding (MIB and SIB1)

  • Frame-to-sample and sample-to-frame conversions to integrate with frame-based processing capabilities in LTE System Toolbox™

  • Support for prototyping algorithms on FPGA and SoC hardware from Xilinx® and Intel®

Simulink Check

Verify compliance with style guidelines and modeling standards

Simulink Check™ provides industry-recognized checks and metrics that identify standard and guideline violations during development. Supported high-integrity software development standards include DO-178, ISO 26262, IEC 61508, IEC 62304, and MathWorks Automotive Advisory Board (MAAB) Style Guidelines. Edit-time checks identify compliance issues as you edit. You can create custom checks to comply with your own standards or guidelines.

Simulink Check provides metrics such as size and complexity that you can use to evaluate your model’s architecture and compliance to standards. A consolidated metrics dashboard lets you assess design status and quality. Automatic model refactoring lets you replace duplicate design elements, reduce design complexity, and identify reusable content.

Support for industry standards is available through IEC Certification Kit (for ISO 26262 and IEC 61508) and DO Qualification Kit (for DO-178).

Key Features

  • Edit-time checking to identify model guideline violations

  • Compliance checking for MAAB style guidelines and high-integrity system design guidelines (DO-178, ISO 26262, IEC 61508, IEC 62304)

  • Compliance checking for secure coding standards (CERT C, CWE, ISO/IEC TS 17961)

  • Custom check authoring with Model Advisor Configuration Editor

  • Metrics for computing model size, complexity, and readability

  • Dashboard providing consolidated view of metrics and project status

  • Model refactoring with clone detection and model transformations

Simulink Coverage

Measure test coverage in models and generated code

Simulink Coverage™ performs model and code coverage analysis that measures testing completeness in models and generated code. It applies industry-standard metrics such as decision, condition, modified condition/decision coverage (MCDC), and relational boundary coverage to assess the effectiveness of simulation testing in models, software-in-the-loop (SIL), and processor-in-the-loop (PIL). You can use missing coverage data to find gaps in testing, missing requirements, or unintended functionality.

Simulink Coverage produces interactive reports showing how much of your model, C /C++ S-functions, MATLAB functions, and code generated by Embedded Coder has been exercised. You can highlight coverage results in blocks and subsystems to visualize gaps in testing. To assess testing completeness, you can accumulate coverage data from multiple test runs. You can apply filters to exclude blocks from coverage and justify missing coverage in reports.

Support for industry standards is available through DO Qualification Kit and IEC Certification Kit.

Key Features

  • Coverage analysis and reports from tests performed on Simulink models (including C/C++ S-functions)

  • Coverage analysis of C/C++ code generated by Embedded Coder

  • Coverage metrics including decision, condition, MCDC, and relational boundary

  • Signal range and complexity metrics, including cyclomatic complexity

  • Coverage result highlighting in blocks, subsystems, and state charts

  • Filtering to exclude model elements from coverage and justify missing coverage

  • Tool qualification support (with DO Qualification Kit and IEC Certification Kit)

Simulink Requirements

Author, manage, and trace requirements to models, generated code, and test cases

Simulink Requirements™ lets you author, analyze, and manage requirements within Simulink. You can create rich text requirements with custom attributes and link them to designs, code, and tests. Requirements can be imported from external sources and you can receive automatic notification when requirements change. You can view the requirements and design together, establish links with drag and drop, annotate diagrams with requirements content, analyze requirements traceability, and navigate between requirements, designs, generated code, and tests.

Simulink Requirements indicates when changes occur to linked requirements, designs, or tests. It calculates the implementation and verification status of your requirements, enabling you to assess project completeness.

Key Features

  • Requirements Editor for requirements authoring, editing, and organization

  • Requirements Perspective for viewing, linking, and managing requirements within the Simulink graphical editor

  • Requirements import and synchronization from third-party tools such as Microsoft® Word and Microsoft Excel®

  • Change tracking and differencing to automatically identify and manage changing requirements

  • Consolidated status metrics for requirements implementation and verification

  • Reports documenting requirement attributes, traceability, and status

  • Bidirectional traceability between requirements, designs, generated code, and tests

Text Analytics Toolbox

Analyze and model text data

Text Analytics Toolbox™ provides algorithms and visualizations for preprocessing, analyzing, and modeling text data. Models created with the toolbox can be used in applications such as sentiment analysis, predictive maintenance, and topic modeling.

Text Analytics Toolbox includes tools for processing raw text from sources such as equipment logs, news feeds, surveys, operator reports, and social media. You can extract text from popular file formats, preprocess raw text, extract individual words, convert text into numerical representations, and build statistical models.

Using machine learning techniques such as LSA, LDA, and word embeddings, you can find clusters and create features from high-dimensional text datasets. Features created with Text Analytics Toolbox can be combined with features from other data sources to build machine learning models that take advantage of textual, numeric, and other types of data.

Key Features

  • Text preprocessing and normalization

  • Machine learning algorithms, including latent Dirichlet allocation (LDA) and latent semantic analysis (LSA)

  • Word-embedding training, and pretrained model import from word2vec, FastText, and GloVe

  • Word cloud and text scatter plots

  • Document import from PDF and Microsoft Word files

  • TF-IDF and word frequency statistics

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