NXP Model-Based Design Toolbox for SPT - version 1.8.0

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Product Release Announcement

Automotive Embedded Systems

NXP Model-Based Design Toolbox

for SPT – version 1.8.0

The Automotive Embedded Systems, Model-Based Design Tools Team at NXP Semiconductors, is pleased to announce the release of the Model-Based Design Toolbox for SPT version 1.8.0. This product allows fast prototyping of algorithms for RADAR directly from MATLAB environment via SPT bit-exact Simulator.

Target audience:

This product is part of the Automotive SW – Model-Based Design Toolbox.

FlexNet Location:

https://nxp.flexnetoperations.com/control/frse/download?element=4014038

Technical Support:

NXP Model-Based Design Toolbox for SPT issues will be tracked through the NXP Model-Based Design Tools Community space.

https://community.nxp.com/community/mbdt

Release Content:

Update the MATLAB SPT Toolbox to support the latest versions for the SPT 3.x variants. The new core simulator version is now SPT Simulator 1.3.4.
Add support for SPT 3.8, which includes also support for the new FLEX PDMA compression.
Add the possibility to set the LFSR value for PDMA instruction.
Support for MATLAB versions:

Windows: R2017b or newer
Linux: R2018b or newer

More than 310+ examples showcasing the supported functionalities.

For more details, features, and how to use the new functionalities, please refer to the Release Notes and Quick Start Guides documents attached.

MATLAB® Integration:

The NXP Model-Based Design Toolbox extends the MATLAB® experience by allowing customers to evaluate and use NXP SPT Accelerator from NXP’s Radar Processors. NXP Model-Based Design Toolbox for SPT version 1.8.0 is fully integrated with MATLAB® environment.

Target Audience:

This release is intended for technology demonstration, evaluation purposes, and prototyping with S32R microprocessors and various flavors of SPT 2.x and 3.x for:

All users for SPT 2.x and 3.x Bit-Exact SPT Simulator versions
NXP customers that want to create their custom SPT kernels, for reasons like data type precision impact, algorithm performance, proprietary algorithms, protection of SW IP, to cover more scenarios, or may simply wish to start with a radar algorithm development from the MATLAB environment.

Useful Resources:

Examples, Trainings, and Support: https://community.nxp.com/t5/MBDT-for-RADAR/bd-p/mbdt-for-radar?tid=communityMBDT