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Machine learning (ML) applications are expanding rapidly inside vehicles, leveraging sensor and vehicle-generated data to continually improve performance and offer new insights and capabilities. However, ML algorithms vary greatly in their composition and runtime requirements, making their deployment on embedded processors complex and time-consuming.
The NXP eIQ Auto ML software development environment offers a consistent and flexible workflow that is designed to provide high-performance and rapid deployment of ML algorithms across the range of NXP S32 automotive processors for diverse applications such as predictive maintenance, enhanced battery management, ADAS, touch sensing and more.
eIQ® Auto ML Software Development Environment flexibility allows for deployment of ML Algorithms of all types from very low to very high compute resource need. This unified ML Software Development Environment, by selecting the appropriate MCU/MPU, addresses a very wide range of applications including:
eIQ Auto supports models from TensorFlow (Protobuf and Keras), PyTorch, as well as the ONNX interchange format and TensorFlow Lite (support varies depending on the underlying platform and backends).
eIQ Auto supports a range of inference engines including multiple open source offerings, hardware specific engines and a proprietary option developed under an ASPICE process. All the engines are unified under the common eIQ Auto model preparation workflow and runtime API.
Yes. The eIQ Auto runtime supports heterogenous execution with multiple runtimes and multiple processor cores. We can support many scenarios, configured using the eIQ Auto model preparation tools and executed with the eIQ Auto runtime libraries:
Yes. The core eIQ Auto runtime libraries and certain inference engines are supported on x86 Linux, allowing you to fully prototype and evaluate your application on your host processor with eIQ Auto APIs before embedded deployment.
Yes. eIQ Auto has interfaces to support both deep learning and classical machine learning algorithms, as well as support for custom operations being executed by the eIQ Auto runtime.
Yes. The eIQ Auto installation includes a set of tutorials demonstrating the end-to-end development flow including model preparation on the host and verification of the runtime application, a set of demos showing more advanced features of the runtime, and a model zoo with additional application examples.
Learn about the eIQ Machine Learning Software and eIQ Auto and explore inference engine options for S32G, i.MX, i.MX RT and S32V processors.
Learn to implement and configure the NXP eIQ® Auto deep learning toolkit to optimize and implement DL without the need for customized hardware expertise.
Explore ways of dealing with these challenges and view an example of an optimized workflow for deploying deep learning in automotive production vehicles using the NXP eIQ® auto deep learning toolkit.
Learn more about the processing efficiency, accelerated development and deployment workflows for AI automotive applications as well as how eIQ Auto Deep Learning toolkit assists your application development.
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