Three-Layer AI Architecture: A Scalable and Efficient Platform for Real-Time, Self-Optimizing AI Applications
The three-layer AI architecture distributes AI algorithm workloads across different layers, allocating computing resources based on each layer's functionality and hardware capabilities. This enables a high-performance, real-time, and continuously self-optimizing AI application platform.

- Sensor AI: Our sensor features a compact AI model to perform basic data preprocessing and anomaly detection. This reduces unnecessary data transmission, easing network bandwidth and backend load.
- Gateway AI: Centralize data from multiple sensors and process complex model inference. Raw data is also preserved for backend training and traceability. Supports parallel multi-tasking and flexible device scalability.
- Server AI: Centrally manages all edge devices and AI model versions. Executes model retraining and automated deployment. Integrates data across devices and sites for high-level logic computation and decision-making.

This architecture offers several technical advantages:
- Layered computing design and task distribution
- Large-scale real-time data processing
- Flexible product development and AI model deployment
- High system stability and maintenance efficiency
- Continuous AI learning and automatic model updates

Compared to traditional centralized AI systems, the three-layer architecture is better suited to handle high data volumes and multi-sensor environments. It enhances computational efficiency and system availability, making it ideal for industries such as smart manufacturing, industrial automation, smart cities, and IoT that demand real-time data processing. It is the optimal solution for enterprises aiming to achieve AI-driven transformation.