Welcome to Edge AI Libraries - highly optimized libraries, microservices, and tools designed for building and deploying real-time AI solutions on edge devices.
If you are an AI developer, data scientist, or system integrator, these assets help you organize data, train models, run efficient inference, and deliver robust, industry-grade automation systems for computer vision, multimedia, and industrial use cases.
These flagship components represent the most advanced, widely adopted, and impactful tools in the repository:
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Build efficient media analytics pipelines using streaming AI pipelines for audio/video media analytics using GStreamer for optimized media operations and OpenVINO for optimized inferencing
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Create 3D/4D dynamic digital twins from multimodal sensor data for advanced spatial analytics.
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Build computer vision AI models enabling rapid dataset management, model training, and deployment to the edge.
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Deploy visual anomaly detection with this state-of-the-art library, offering algorithms for segmentation, classification, and reconstruction, plus features like experiment management and hyperparameter optimization.
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Optimize, run, and deploy AI models with this industry-standard toolkit, accelerating inference on Intel CPUs, GPUs, and NPUs. Supports a broad range of AI solutions including vision-based applications, generative AI, and vision-language models.
This group provides core AI libraries and tools focused on computer vision model building, training, optimization, and deployment for Intel hardware. They address challenges such as dataset curation, model lifecycle management, and high-performance inference on edge devices.
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Build computer vision AI models enabling rapid dataset management, model training, and deployment to the edge.
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Deploy visual anomaly detection with this state-of-the-art library, offering algorithms for segmentation, classification, and reconstruction, plus features like experiment management and hyperparameter optimization.
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Software for efficient model training and deployment.
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Toolkit for optimizing and deploying AI inference, offering performance boost on Intel CPU, GPU, and NPU devices.
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OpenVINO Training Extensions & Model API
A set of advanced algorithms for model training and conversion.
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Dataset management framework to curate and convert vision datasets.
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Create, tailor, and implement custom AI models directly on edge platforms.
Handling large-scale media analytics workloads, these components support real-time audio and video AI pipeline processing, transcription, and multimodal embedding generation, addressing common use cases like surveillance, content indexing, and audio analysis.
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Deep Learning Streamer & Deep Learning Streamer Pipeline Server
Streaming AI pipeline builder with scalable server for media inferencing.
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Microservices providing real-time audio transcription and intelligence extraction.
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VLM Inference Serving & Multimodal Embedding
Services handling vision-language models and embedding generation for multimodal search.
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Software for creating dynamic 3D/4D digital twins for spatial analytics.
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Time Series Analytics Microservice
Real-time analytics microservice designed for anomaly detection and forecasting on sensor time-series data.
Efficient data management and retrieval are crucial for AI performance and scalability. This group offers components for dataset curation, vector search, and document ingestion across multimodal data.
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Vector Retriever (Milvus) & Visual Data Preparation (Milvus and VDMS)
High-performance vector similarity search and visual data indexing.
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Model Registry & Document Ingestion
Tools for managing AI model versions and preparing documents for AI workflows.
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Splits/segments video streams into chunks, supporting batch and pipeline-based analytics.
These support tools provide visual pipeline evaluation and performance benchmarking to help analyze AI workloads and industrial environments effectively.
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Visual Pipeline and Platform Evaluation Tool
Benchmark and analyze AI pipeline performance on various edge platforms.
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Compliance and performance testing toolkit for motion control.
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Showcase, monitor, and optimize the scalability and performance of AI workloads on Intel edge hardware. Configure models, choose performance modes, and visualize resource metrics in real time .
Focused on real-time industrial automation, motion control, and fieldbus communication, these components provide reliable, standards-compliant building blocks for manufacturing and factory automation applications.
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ECAT EnableKit & EtherCAT Masterstack
EtherCAT communication protocol stack and development tools.
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Libraries implementing motion control standards for servo drives and real-time trajectory management.
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Tools for data communication in automation networks.
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Microservice for collecting, processing, and distributing real-time sensor and industrial device data; supports both edge analytics and integration with operational systems.
Optimized libraries for robotic perception, localization, mapping, and 3D point cloud analytics. These tools are designed to maximize performance on heterogeneous Intel hardware using oneAPI DPC++, enabling advanced robotic workloads at the edge.
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FLANN optimized with oneAPI DPC++
High-speed nearest neighbor library, optimized for Intel architectures; supports scalable feature matching, search, and clustering in robotic vision and SLAM.
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Efficient ORB feature and descriptor extraction for visual SLAM, mapping, and tracking; designed for multicamera and GPU acceleration scenarios.
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Point Cloud Library Optimized with Intel oneAPI DPC++
Accelerated modules from PCL for real-time 2D/3D point cloud processing—supports object detection, mapping, segmentation, and scene understanding in automation and robotics.
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Unified interface library bridging motion control commands between AI modules and industrial/robotic devices; simplifies real-time control integration and system interoperability in mixed hardware environments.
Ready-to-use example applications demonstrating real-world AI use cases to help users get started quickly and understand integration patterns:
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Conversational AI application integrating retrieval-augmented generation for question answering.
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Conversational AI application integrating retrieval-augmented generation for question answering.Optimized for Intel(R) Core.
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AI pipeline for automated summarization of textual documents.
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Video Search and Summarization
Application combining video content analysis with search and summarization capabilities.
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Edge Developer Kit Reference Scripts
Automate the setup of edge AI development environments using these proven reference scripts. Quickly install required drivers, configure hardware, and validate platform readiness for Intel-based edge devices.
Visit the Edge AI Suites repository for a broader set of sample applications targeted at specific industry segments.
Specialized microservices delivering machine learning-powered analytics optimized for edge deployment. These microservices support scalable anomaly detection, classification, and predictive analytics on structured and time-series data.
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An efficient Isolation Forest microservice for unsupervised anomaly detection supporting high-performance training and inference on tabular and streaming data.
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High-speed Random Forest microservice for supervised classification tasks, optimized for edge and industrial use cases with rapid training and low-latency inference.
To learn how to contribute to the project, see CONTRIBUTING.md.
If you need help, want to suggest a new feature, or report a bug, please use the following channels:
- Questions & Discussions: Join the conversation in GitHub Discussions to ask questions, share ideas, or get help from the community.
- Bug Reports & Feature Requests: Submit issues via Github Issues for bugs or feature requests.
The Edge AI Libraries project is licensed under the APACHE 2.0 license, except for the following components:
| Component | License |
|---|---|
| Dataset Management Framework (Datumaro) | MIT License |
| Intel® Geti™ | Limited Edge Software Distribution License |
