Edge AI in Action: Practical Approaches to Developing and Deploying Optimized Models
Image credit: CVPR 2024Edge AI is a term that refers to the application of artificial intelligence on edge devices, i.e., devices that are at the periphery of a network, such as smartphones, tablets, laptops, cameras, sensors, and drones, among others. Edge AI enables these devices to perform AI tasks autonomously, without relying on a connection to the cloud or a central server. This brings benefits such as higher speed, lower latency, greater privacy, and lower power consumption.
However, edge AI also poses many challenges and opportunities for model development and deployment, such as size reduction, compression, quantization, and distillation. Edge AI also involves integrating and communicating between edge devices and the cloud or other devices, creating a hybrid and distributed architecture.
In this tutorial, we provided clear and practical guidance on developing and deploying optimized models for edge AI. Our comprehensive approach covered both the theoretical and technical aspects, along with best practices and real-world case studies. Our primary focus was on computer vision and deep learning models, as they are highly relevant to edge AI applications. Throughout the tutorial, we demonstrated the utilization of various tools and frameworks, including TensorFlow, PyTorch, ONNX, OpenVINO, Google Mediapipe, and Qualcomm SNPE.
Additionally, we provided concrete examples of multimodal AI applications, including head pose estimation, body segmentation, hand gesture recognition, sound localization, and more. These applications leverage various input sources, such as images, videos, and sounds, to create highly interactive and immersive edge AI experiences. Our presentation encompassed the development and deployment of these multimodal AI models on Jabra collaboration business cameras. Furthermore, we explored integration possibilities with cloud services and other devices, such as AWS DeepLens, Luxonis OAK-1 MAX, and NVIDIA Jetson Nano Developer Kit.
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