What is edge AI?

How does edge AI work?

Edge AI uses machine learning algorithms, neural networks, and deep learning to train models to accurately perform computations on (data from) edge devices like Internet of Things (IoT) devices, routers, switches, cameras, and sensors. In addition to traning models, edge AI can be used to run model inferencing. Edge AI models improve over time. If problems within the model arise, data is transferred to the cloud for additional training. This replaces the inference engine at the edge and improves the model’s performance.

What are the components of edge AI?

Edge AI systems include the following:

• Edge devices: devices at the edge of the network, where data is generated. Examples include IoT devices, surveillance cameras, industrial sensors, and autonomous vehicles.
• Edge computing: Edge AI relies on edge computing infrastructure to process and analyze data locally on the edge devices or nearby servers, reducing dependence on the cloud for data processing.
• Machine learning models: pre-trained models deployed on edge devices to analyze and interpret data in real-time.
• Data filtering and preprocessing: data remains local to reduce bandwidth, and only relevant information is sent to the cloud.

What are the advantages of edge AI?

Edge AI offers multiple advantages, including reduced latency, improved data privacy and security, and minimized bandwidth usage.

• Real-time data processing at the edge: Traditional cloud-based AI models require sending data to the cloud for processing, which can introduce latency. Edge AI processes data locally to speed response time and enable real-time decision-making. This is particularly beneficial in applications such as autonomous vehicles, industrial automation, and smart cities, where instant data processing is critical. Edge AI can reduce latency in decision making and increase speed.
• Enhanced privacy and security: Processing data locally on edge devices instead of sending it to the cloud can reduce data privacy concerns. By keeping sensitive information within the local network, edge AI ensures data remains secure and confidential.
• Reduced reliance on cloud resources: Reduce reliance on cloud resources and continuous cloud connectivity. This minimizes bandwidth requirements and lowers operational costs.

What is the difference between edge AI and public cloud-based AI?

Cloud-based AI systems rely on a cloud-based infrastructure to centrally process vast amounts of data using machine learning algorithms. Latency and cost can be an issue when large volumes of data are sent to the cloud.

Edge AI uses a decentralized approach. Data processing and analysis occurs on edge devices such as routers, switches, servers, and IoT devices, reducing costs and enabling real-time decision-making. Edge AI performance remains limited by the device’s size and processing capacity.

What are some examples of edge AI use cases?

Wearable devices, self-driving cars, real-time traffic updates, security cameras, and smart home appliances use edge AI capabilities to provide users with real-time information, quickly. Industries like retail, manufacturing, and energy are also using edge AI to reduce costs, streamline processes, and automate whenever process to gain efficiencies.

Retail is finding success with smart technology, like shopping carts with sensors, and smart check-outs that use edge AI technology to improve and speed up the customer experience.

Manufacturing uses edge AI to drive efficiency and productivity with the use of edge AI technology. With the use of predictive maintenance, teams can identify issues and forecast problems, as well as implement timely resolution to prevent unnecessary downtime. In addition, edge AI can improve optimization, supply chain analytics, quality control and worker safety.

Energy used edge AI to save money with the creation of a sustainable infrastructure to drive efficient resource management. Edge AI can improve distribution, predict demand, and maximize grid efficiency by analyzing historical data, weather patterns, and energy health data together. Smart grids use AI-equipped edge to identify anomalies and enhance the flow of energy in real-time.