What is Edge AI?

“Edge artificial intelligence” refers to the deployment of artificial intelligence (AI) algorithms and models directly on edge devices, such as smartphones, Internet of Things (IoT) devices, sensors, and other embedded systems, instead of relying solely on centralised cloud servers for processing.

Traditionally, AI tasks requiring complex computations were performed in remote data centres or cloud servers due to their high computational requirements and the need for substantial resources. However, this approach often introduces latency, consumes significant network bandwidth, and raises concerns about data privacy and security, especially when dealing with sensitive information. Edge AI addresses these issues by bringing AI capabilities directly to the edge devices where data is generated and consumed. This means that data processing, analysis, and decision-making occur locally on the device itself, without needing to transmit data to a central server for processing. This approach offers several benefits:

Reduced Latency: By processing data locally, edge AI reduces the time it takes for data to be analysed and acted upon, improving response times for real-time applications.
Improved Privacy and Security: Since data stays on the device and is not transmitted over a network, edge AI can enhance privacy and security by minimizing the risk of data breaches or unauthorised access.
Bandwidth Efficiency: Edge AI reduces the need to transfer large volumes of data to centralised servers, leading to lower bandwidth requirements and potentially reducing costs associated with data transmission.
Offline Functionality: Edge AI enables devices to perform AI tasks even when they are not connected to the internet, enhancing functionality in environments with limited or intermittent connectivity.
Scalability: Edge AI distributes computational load across multiple devices, allowing for scalable AI applications without overburdening centralised servers.

Common applications of edge AI include real-time video analytics, predictive maintenance in industrial equipment, smart home devices, autonomous vehicles, and wearable health monitors, among others.