AI Edge Sensing and Computing System
The AI Edge Computing Sensor System ‒ Xink Nano Edge AI from eYs3D Microelectronics made a dazzling debut by winning Gold in the Smart Innovation Award. Featuring integration of multi-sensor fusion, real-time AI recognition, and edge computing architecture, the system is highly versatile and scalable, making it ideal for a wide range of applications including smart transportation, autonomous robots, drones, security surveillance, and intelligent environment sensing.
Designed for on-site real-time response and low-latency AI processing, the system delivers modular flexibility, scalability, and high privacy protection ‒ critical features for smart scenarios requiring instant decision-making.
Xink Nano Edge AI adopts an AI Hybrid Model architecture and is powered by the eCV 4 series SoC, which includes a dual-core ARM Cortex-A55 processor. It supports widely used AI models such as YOLOv8 and pose estimation, and integrates 3D RGB imaging, ToF (Time-of-Flight) depth sensors, and advanced algorithms to enable object recognition, status evaluation, and anomaly detection. Key advantages include low power consumption, minimal maintenance, and fast response times.
The system also leverages lightweight model computing and an autonomous data generation and training process, which effectively reduces dependency on high-compute resources, improves training efficiency, and enhances deployment flexibility. This allows AI models to run quickly at the edge while maintaining accuracy and data security.
As many generic AI models today are too large and compute-intensive for efficient edge deployment, eYs3D Microelectronics offers AI model customization services, specifically optimized for edge use. These services help clients prune, compress, and retrain models based on their specific application scenarios, and can also incorporate autonomous data generation to boost model precision and adaptability.
Through this service, customers can achieve compact, high-efficiency, and low-latency AI model deployment, significantly reducing reliance on cloud resources while improving system responsiveness and data privacy—truly enabling the realization of smart edge applications.
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