What is a typical architecture for an edge AI system?

A typical edge AI system architecture consists of three primary layers: hardware for data capture and processing, software for running AI models, and communication protocols to handle data flow. The goal is to enable local decision-making on devices like sensors, cameras, or embedded systems, reducing reliance on cloud connectivity. This design balances performance, latency, and resource constraints while maintaining scalability for diverse applications.

The hardware layer includes sensors (e.g., cameras, microphones) to collect data and processors optimized for AI workloads. Devices like Raspberry Pi, NVIDIA Jetson modules, or specialized AI chips (Google Coral TPU) are common. These components handle initial data preprocessing (resizing images, noise filtering) and execute lightweight machine learning models. For example, a security camera might use a vision processor to run a person-detection model locally before sending alerts. Edge servers—small-scale compute nodes—can act as intermediaries for more complex tasks, like aggregating data from multiple sensors in a factory.

The software layer involves frameworks like TensorFlow Lite, PyTorch Mobile, or ONNX Runtime to deploy models optimized for edge devices. Developers convert trained models to edge-friendly formats using techniques like quantization (reducing numerical precision from 32-bit to 8-bit) or pruning (removing redundant neural network nodes). A voice assistant might use a pruned keyword-spotting model to detect “wake words” offline. Edge-specific middleware, such as AWS IoT Greengrass or Azure Edge Manager, handles tasks like model updates, device monitoring, and local inference scheduling. For instance, a drone could use these tools to switch between obstacle-avoidance and navigation models based on battery life.

The communication layer connects edge devices to each other or to the cloud using protocols like MQTT, HTTP/2, or LoRaWAN. Hybrid architectures often split tasks: a smart thermostat might process temperature data locally but send diagnostics to the cloud weekly. Security measures like TLS encryption and hardware-based trusted execution environments (Intel SGX) protect data and models. For example, a medical wearable could encrypt patient vitals before transmitting them to a hospital edge server. This layered approach ensures responsiveness for time-sensitive applications while allowing flexibility for updates and scalability.