The left side demonstrates the architecture of a traditional AI camera system. In such a system, the camera delivers images to the Raspberry Pi. The Raspberry Pi processes the images and then performs AI inference. Traditional systems may use external AI accelerators (as shown) or rely exclusively on the CPU.

The right side demonstrates the architecture of a system that uses IMX500. The camera module contains a small Image Signal Processor (ISP) which turns the raw camera image data into an input tensor. The camera module sends this tensor directly into the AI accelerator within the camera, which produces an output tensor that contains the inferencing results. The AI accelerator sends this tensor to the Raspberry Pi. There is no need for an external accelerator, nor for the Raspberry Pi to run neural network software on the CPU.

Compatible with all Raspberry Pi computers, based on the IMX500 Intelligent Vision Sensor to help users create impressive vision AI applications and neural network models.

The IMX500's tight integration with Raspberry Pi's camera software stack allows users to deploy their own neural network models. To get started, the Raspberry Pi Official has developed a guide to get you running either a pre-packaged or custom-made neural network model on the camera.

Create AI models and applications for the IMX500 Intelligent Vision Sensor and Raspberry Pi, then scale up to an enterprise deployment with AITRIOS.

Local Studio walks you through the necessary steps to efficiently label, train and produce AI models.