Connects - Training Material

Discussions

Artificial intelligence and machine learning (AI/ML) are revolutionizing the industrial world. For maximal impact, AI/ML must be done close to where data is generated and the output of analysis used. It's an ideal workload for edge computing and complements NXP’s EdgeScale cloud-based device-management platform. Layerscape processors are well suited to hosting AI/ML workloads. Software from NXP and third parties helps enable developers to create industrial applications using AI/ML technology. These applications can be distributed, Layerscape-based edge nodes with endpoints performing multiple tasks including: addressing condition monitoring or first-level classification; running popular edge frameworks to deliver cloud-like services on premises, aggregating data from multiple endpoints or performing additional analysis; running cloud-based software analyzing data for long-term trends.

S32 Design Studio 3.0 S32V234 focus on configuration tools pin wizard, clocks config, with SDK configuration, FreeRTOS configuration and use building and running a project showing debug with S32 debugger on the m4 with code on target. Configuration tools suite formed from pins, clocks, peripherals, DCD, IVT are offering a complete view of the platform with complete access to all the details directly from the tool and possibility to generate, build and debug the applications.

Learn about vision processing and technical capabilities of S32V’s dual-APEX acceleration engines, multi-core A53 processors, on-chip image signal processor (ISP) which differentiates S32V from other Arm based processors that can’t meet the same performance/power envelope. Attendees will get a demonstration of the graph tools available on our S32 Design Studio IDE that facilitate the creation of graphs for the APEX and ISP Engines.

A key challenge to the deployment of Edge compute IoT is management and security of 1000s to millions of Edge compute nodes. EdgeScale simplifies this by providing a cloud-based management framework leveraging the hardware security features of Layerscape processors. Learn how you can use EdgeScale to manage a heterogenious set of Edge compute nodes, and use it with your choice of Edge compute framework - AWS Greengrass, Azure IoT Edge and others.

This presentation provides an overview of the new toolchain to support the S32 platform of MCUs and application processors, this includes an IDE with new project generation, extensive collateral for ease of use and getting started as well as the next-generation Debugger tools, a new Flash tool specifically design for the S32 platform, the S32 configuration tool plus numerous software products integarted into the toolchain for ease of customer use.

This session will go into the detail of the software enablement around the BlueBox v2.0, aka “BlueBox Mini” or “BLBX2”. Suited for level 2/3 autonomous drive. From an engineering perspective topics will include: 1. The BlueBox v2.0 hardware layout. 2. BSP content with middleware; Linux config and Ubuntu rootfs; Linux options between and inter-communication between them; Discussion of middleware options (including ROS versions, and others). 3. How ADAS is supported. 4. Combined to achieve level 2/3 ADAS with AutonomouStuff. 5. Apollo versions ported and benchmarking of the planning module. 6. Future offerings with hardware support and systems-level safety and orchestration. 7. Technical lecture explaining the internals of this enablement.

Many people involved in the electronics industry need a basic knowledge of semiconductor components in order to understand issues like manufacturing cycle time, semiconductor fab consolidation and failure analysis results. This session is geared towards a non-technical audience and gives a high-level overview of semiconductor devices and how they are made. The lecture begins with a brief overview of how semiconductor devices work. The bulk of the presentation describes the generic manufacturing flow for semiconductor devices with an emphasis on the complexity of the equipment/facilities used and the process time required.

Automotive Ethernet PHYs enable future driving scenarios by providing the necessary physical throughput power. While software brings added value for security, diagnosis, energy efficiency and safety. As the functional requirements for automotive PHY drivers differ from general purpose applications, additional functionality is needed to closely monitor and diagnose the device and to create advanced use cases like Layer-1 wake-up and sleep. In this session, these specific requirements for automotive Ethernet PHY SW are discussed. Existing PHY SW frameworks such as Linux or AUTOSAR are introduced. Finally, the session will touch upon typical application scenarios which require interaction with PHY SW.

Connects - Training Material

Connects - Training Material

Training Sessions Material

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