Edge AI solutions are Artificial intelligence systems that perform data processing on local devices instead of sending data to central servers or the cloud. Edge AI technology enables real-time data analysis at the edge of the network, i.e. on end devices such as smartphones, cameras, sensors, or robots. Thanks to this technology, the systems offer low latency, greater privacy, and lower costs for transferring data to the cloud.
Edge AI Solutions
Type of technology
Description of the technology
Basic elements
- Edge devices: Local devices that process data, such as cameras, smartphones, and IoT sensors.
- AI algorithms: Artificial intelligence models that run locally on devices, analysing data in real time.
- Network infrastructure: Networks that enable connectivity between edge devices and the central server.
- Real-time processing: Edge AI algorithms operate with minimal latency, which is crucial for many applications.
- Mechanisms for updating models: They enable regular updates and improvements to local AI models.
Industry usage
- Autonomous vehicles: Quick analysis of images from cameras and sensors to make real-time decisions.
- Health care: Monitoring patients in real time using edge devices, such as smart wristbands.
- Industry 4.0: Management and monitoring of industrial machinery on production lines.
- Smart cities: Smart management of urban infrastructure, such as traffic monitoring and lighting management.
- Retail: Automatic goods recognition and inventory management in shops.
Importance for the economy
Edge AI solutions are crucial for industry, medicine, automotive, and many other sectors where fast real-time data analysis is needed. With Edge AI, companies can avoid the cost of sending data to the cloud, minimise analytics latency, and enhance data privacy and security. These technologies are revolutionising industries such as urban infrastructure management, manufacturing, health care, logistics and the autonomous vehicle industry.
Related technologies
Big Data
Edge systems can analyse large amounts of data locally before the data is sent to the cloud.
Automation
It supports autonomous systems and robots, enabling rapid analysis and decision-making at the local level.
Mechanism of action
- Edge AI solutions are based on processing data locally on edge devices without the need to send it to central servers or the cloud. Input data, such as camera images or sensor data, is processed by local AI models that analyse and make decisions in real time. This enables the systems to operate with low latency, which is crucial for applications such as autonomous vehicles, patient health monitoring, and industrial infrastructure management. Regular model updates can be provided from central servers, but most processing is done at the edge of the network.
Advantages
- Processing speed: Real-time data analysis without having to upload to the cloud.
- Lower delays: Critical decisions can be made instantly, which is crucial in applications such as autonomous vehicles.
- Cost savings: Reducing the cost of data transfer and cloud usage.
- Greater privacy: Data can be processed locally, minimising the risk of data loss or privacy violations.
- Scalability: Edge AI solutions can run on multiple devices simultaneously, without the need to expand cloud infrastructure.
Disadvantages
- Implementation costs: Large-scale implementation of Edge AI can be costly due to hardware requirements.
- Technical complexity: Implementing and maintaining edge solutions require advanced technical expertise.
- Data security: Although data is processed locally, edge devices can be vulnerable to hacking attacks.
- Model updates: AI models running locally may require regular updates, which can be difficult to manage in distributed systems.
- Limited computing power: Edge devices may have limited computing power compared to central servers.
Implementation of the technology
Required resources
- Edge devices: Cameras, IoT sensors, smartphones, and other local data devices.
- Local computing power: High-performance computing chips, such as graphics processing units (GPUs) or TPUs that can run on edge devices.
- AI algorithms: Artificial intelligence models optimised to run on devices with limited resources.
- Software: Tools to manage and update local AI models and to synchronise with central systems.
- Network resources: Stable network connection to synchronise data between edge devices and central servers.
Required competences
- Machine learning: Knowledge of techniques used to train and optimise AI models running on edge devices.
- Embedded programming: Ability to work with embedded software on devices with limited resources.
- Network management: Knowledge of the network infrastructure that connects edge devices to central systems.
- Data security: Ability to manage the security of locally processed data on edge devices.
- Algorithm optimisation: Ability to optimise AI models for energy and computational efficiency.
Environmental aspects
- Energy consumption: Local data processing can require significant energy resources, especially in industrial equipment.
- Emissions of pollutants: The production and operation of edge devices can contribute to CO2 emissions.
- Raw material consumption: Edge devices require advanced materials, such as integrated circuits and rare earth metals.
- Recycling: Edge devices can be difficult to recycle, which generates electronic waste.
- Water consumption: Production processes associated with edge devices can contribute to water consumption, especially in equipment cooling.
Legal conditions
- Legislation governing the implementation of solutions, such as AI Act (example: regulations on the implementation and accountability for the operation of Edge AI–based autonomous systems).
- Safety standards: Regulations for security of data and systems that process information locally on edge devices (example: ISO/IEC 27001).
- Intellectual property: Protection of AI algorithms and data processed on edge devices (example: copyright on AI-based solutions).
- Data security: Regulations for the protection of personal data processed locally on devices (example: GDPR in the European Union).
- Export regulations: Restrictions on the export of advanced devices and edge processing technologies to sanctioned countries (example: regulations for the export of IoT and Edge AI technologies).