技术日 - 培训资料

讨论

A key challenge to the deployment of Edge compute IoT is management and security of 1000s to millions of Edge compute nodes and the software that is running on them. NXP’s EdgeScale technology simplifies this by providing a cloud-based management framework leveraging the hardware security features of NXP Layerscape and i.MX processors. This instructor-led session will demonstrate how to install AWS Greengrass and Azure IoT Edge software on NXP processors using the NXP EdgeScale cloud deployment service, integrate hardware based security and deploy a simple application that collects data, processes it locally and sends updates to the cloud. This session will provide greater understanding of edge computing and cloud computing concepts and technologies.

The i.MX 8M was designed with consumer and industrial customers in mind. The i.MX 8M series of processors are the first in a completely new paradigm of processors designed targeting market leading performance for audio, video and voice. The i.MX 8M starts with a system solution in mind and drives optimized solutions (best in class performance, optimized system cost, built with Google/Amazon/Msft ecosystems in mind) for many different customers from HMI, audio, video, voice, IOT, digital signage, printers, machine vision, etc. Best in class 4K video with HDR, best in class audio interfaces, low-power and optimized for lower cost. Come see what this new family of processors is all about and learn where we are headed.

Join this session to get a glimpse of where you will see i.MX 8/8X in the near future and why graphics, ML, AI, video, image processing, vision, connectivity, audio and voice functions make the i.MX 8 series ideal for Automotive and Industrial applications. Learn about the i.MX 8/8X families that are comprised of common subsystems establishing an unmatched range of performance scaling with pin-compatible options and the highest level of software reuse. In addition, learn more about the upcoming i.MX 8DualMax product line to help customers meet additional cost performance scaling across the i.MX 8 family.

As memory interface data rates increase, so does the need for ensuring proper margins have been designed into the system’s usage of that interface. One way Micron has studied the system’s usage of the memory interface is through Timing & Signal Analysis (TSA). A TSA attaches physical hardware to the memory device to observe a subset of the memory interface as operated by the system. Another approach to memory interface is Virtual TSA (vTSA). This method uses the memory controller’s training algorithms to provide margin information without the use of physical test hardware. This method allows characterization of the entire memory interface. This session will compare and contrast the two different methods, and will ultimately demonstrate the value of integrating vTSA tools on the system platform. We intend to reference actual results from the latest i.MX 8 QXP board.

Join this session to learn how to leverage and get started with NXP’s power optimized LPC546xx MCU family, which now scales up in performance to 220 MHz. With rich peripheral integration, which includes 21 communication interfaces, including HS USB, dual CAN FD, graphics controller, to package option scalability, these microcontrollers address the various needs of today’s embedded IoT applications. Leave the session with the knowledge and skills to get up and running with the LPC546xx MCU family, which includes our comprehensive ecosystem of software and tools.

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.

Machine learning can performed on a wide range of device categories - from MCUs with Arm® Cortex®-M4 and M7 cores to complex SoCs with high-end A-class cores, GPUs, DSPs, and dedicated machine learning accelerators. The first step is learning how to utilize proper training techniques for model development, but beyond that how to generate optimized inference engines that can be used to perform classifications, anomaly detection, predictions, and other types of decisions. This presentation highlights some basic training techniques, such as data augmentation, but the primary focus will be on various ways to deploy neural network frameworks and classical machine learning algorithms, and most importantly, utilizing a variety of open source tools and techniques. We will show how these techniques fit in with some real use cases such as object recognition and anomaly detection.

技术日 - 培训资料

Technology Days - Training Material

Training Sessions Material

Home

Vehicle Networking

AI and Machine Learning

AI and Machine Learning; Edge Computing

AI and Machine Learning; Voice

Connectivity (UWB, 5G, Wi-Fi 6)

Embedded Applications Software; Human Machine Interface (HMI)

Embedded Software

Human Machine Interface (HMI)

Motor Control

Security

Enabling Technologies

Voice; Human Machine Interface (HMI)

Factory Automation

Wearable; Human Machine Interface (HMI)

Home Control & Security; Development Software & Tools

Automotive

Edge Intelligence

Industrial

IoT Smart Home

Hardware & Software Solutions

Automotive

Edge Intelligence

Industrial

Mobile, Wearable & Computing

IoT Smart Home

Hardware & Software Solutions

Automotive

IoT Smart City

Edge Intelligence

Industrial

Mobile, Wearable & Computing

IoT Smart Home

Hardware & Software Solutions

Station F

Singapore

Automotive

Edge Intelligence

IoT Smart Home

Mobile, Wearable & Computing

Industrial

Hardware & Software Solutions

Automotive

Communication Infrastructure

Edge Intelligence

Industrial

IoT Smart Home

Hardware & Software Solutions

Mobile, Wearable & Computing

Automotive

Edge Intelligence

Hardware & Software Solutions

Industrial

IoT Smart City

IoT Smart Home

Automotive

Automotive

Hardware and Software Solutions

Industrial

IoT Smart Home

Mobile, Wearable, and Computing

Automotive

Edge Intelligence

Hardware & Software Solutions

Industrial

IoT Smart City

IoT Smart Home

Mobile, Wearable & Computing

Automotive

Artificial Intelligence, Enabling Technologies, Security & Solutions

Smart Machinery & Industrial Automation

Smarter World Solutions & Multimode Connectivity

Artificial Intelligence, Enabling Technologies, Security & Solutions

Smart Machinery & Industrial Automation

Smarter World Solutions & Multimode Connectivity

Automotive

Artificial Intelligence, Enabling Technologies, Security & Solutions