Edge AI technologies involve deploying artificial intelligence (AI) models and algorithms on edge devices, such as smartphones, IoT devices, or embedded systems, rather than relying on cloud-based processing. Here's a brief overview of Edge AI:

Languages and tool stacks commonly used for Edge AI development include:

1. Python: A popular language for AI development, with extensive libraries like TensorFlow Lite and PyTorch Mobile for edge deployment.
2. C/C++: Low-level languages that offer better performance and control over hardware resources, suitable for resource-constrained devices.
3. TensorFlow Lite: A lightweight version of TensorFlow designed for mobile and IoT devices, enabling on-device inference.
4. PyTorch Mobile: A mobile-friendly version of PyTorch that allows running AI models on edge devices.
5. OpenCV: An open-source computer vision library that provides algorithms for image and video processing on edge devices.
6. ONNX Runtime: An open-source runtime for deploying ONNX (Open Neural Network Exchange) models on various platforms, including edge devices.

Methods to deploy code updates for Edge AI systems:

1. Over-the-Air (OTA) updates: Deliver software and model updates wirelessly to edge devices, ensuring seamless updates without physical access to the devices.
2. Containerization: Package AI models and dependencies into lightweight containers (e.g., Docker) for easy deployment and updates across different devices.
3. Model compression techniques: Optimize AI models through techniques like quantization, pruning, or knowledge distillation to reduce model size and enable efficient deployment on edge devices.
4. Edge computing platforms: Utilize edge computing platforms (e.g., AWS Greengrass, Azure IoT Edge) that provide frameworks and tools for deploying and managing AI models on edge devices.