Summary
The video showcases the advancements in intelligent Edge Computing enabled by i.MX93 applications processors, focusing on high-efficiency processing, advanced security, and AI technologies. The processors feature an energy-flex architecture and neural engine for mid-level AI applications, supported by the EIQ toolkit. Real-world examples demonstrate AI applications in areas like driver monitoring, facial recognition, and real-time object identification using MPU calls and TensorFlow Lite models for efficient processing and inference. Overall, the video highlights how these technologies are shaping a smarter world with enhanced capabilities in industrial devices and machine learning applications.
Chapters
Introduction to Intelligent Edge Computing
Innovative Energy Flex Architecture of NXP's i.MX93 Applications Processors
Efficient Neural Engine of i.MX93 Applications Processors
EIQ Toolkit Support for i.MX93 SoC
Real-Time Image Processing Example
Driver State Monitoring System for Smart Cars
Face Detection and Recognition System
Object Detection and Recognition
Semantic Image Information Separation
Introduction to Intelligent Edge Computing
The development of intelligent Edge Computing along with an increasing availability of connected devices is creating a smarter world. These innovations require high efficiency processing, advanced security, and AI technologies.
Innovative Energy Flex Architecture of NXP's i.MX93 Applications Processors
The i.MX93 applications processors feature an innovative energy flex architecture that empowers industrial devices with high efficiency processing capabilities.
Efficient Neural Engine of i.MX93 Applications Processors
The i.MX93 applications processors also feature a high efficiency neural engine for mid to entry-level AI and machine learning applications.
EIQ Toolkit Support for i.MX93 SoC
The EIQ toolkit offers full support for the i.MX93 SoC, accelerating related AI and machine learning applications. Various examples demonstrate AI and ML applications with the integrated MPU.
Real-Time Image Processing Example
An evaluation kit (EVK) and a USB camera are used for real-time image processing. The system collects a camera frame, processes it, runs an application on the frame, performs inference, and displays the output on the screen.
Driver State Monitoring System for Smart Cars
A system monitors the driver's state to assist in driving scenarios. It detects abnormal states like smoking or phone calls and alerts the driver in real-time using TensorFlow Lite models and MPU calls.
Face Detection and Recognition System
The system identifies human faces, compares them with existing data, and displays the name if a match is found. If the face is unknown, it enters 'new face mode' for further database updates. The system achieves fast detection and recognition using MPUs.
Object Detection and Recognition
The system captures and recognizes objects in real-time, including human gestures and poses. It leverages the integrated NPU for efficient processing with low inference times.
Semantic Image Information Separation
The system separates the main character and semantic information in images. The main character is highlighted in white, showcasing the system's ability to process image semantics efficiently.
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