Edge AI

Edge AI involves the deployment of artificial intelligence (AI) algorithms and models directly on edge devices. An edge device is a physical, remote computing device that’s connected to the network edge, such as smartphones, IoT devices, and embedded systems. This approach enables smarter, faster, and more secure processing on the devices closest to the data source, and without relying on cloud-based processing.

Edge AI allows responses to be delivered almost instantly. With edge AI, data is processed within milliseconds providing real-time feedback with or without internet connection because AI algorithms can process data closer to the location of the device. This process can be more secure when it comes to data because sensitive data never leaves the edge.

Use cases of Edge AI

Smart Homes, Cities and Infrastructure: Edge AI plays a crucial role in building smarter and more efficient homes and cities, enabling analysis and processing of vast amounts of data from sensors, cameras, and other IoT devices in real time.

Industrial IoT: By embedding AI capabilities into edge devices, such as robots and machines, tasks that require real-time processing and decision-making can be performed locally, resulting in improved productivity, increased safety, and better overall performance.

Autonomous Vehicles: By using real-time processing of data from sensors like cameras, LiDAR, and radar, edge AI enables AI-powered vehicles to make decisions critical for safety and efficiency.

Importance of Edge AI

Edge AI is revolutionizing various industries by bringing advanced computing capabilities directly to the edge. With the increased demand for edge devices to think for themselves, edge AI brings intelligence and real time analytics to even the smallest edge devices.

Edge AI offers several advantages over traditional AI approaches:

• Minimize latency by reducing the time delay involved in sending data to the cloud, crucial for real-time applications.
• Improve overall system performance with real-time data processing for discission critical applications.
• Reduce the power budget and increase battery life to maximize device operation.
• Reduce reliance on cloud connectivity and increase autonomy in remote or network-constrained use cases.
• Enhances privacy and security by avoiding the transmission of data between systems.
• Reduce cost and network congestion by using less bandwidth.

Benefits of edge AI

Less power use: Save energy cost with local data processes and lower power requirements for running AI at the edge compared to cloud data centers

Reduced bandwidth: Reduce the amount of data needed to be sent and decrease costs with more data processed, analyzed, and stored locally instead of being sent to the cloud

Privacy: Lower the risk of  sensitive data getting out with data being processed on edge devices from edge AI

Security: Prioritize important data transfer by processing and storing data in an edge network or filtering redundant and unneeded data

Scalability: Easily scale systems with cloud-based platforms and native edge capability on original equipment manufacturer (OEM) equipment

Reduced latency: Decrease the time it takes to process data on a cloud platform and analyze it locally to allow other tasks