In recent years edge computing has been gaining traction in Internet of Things related applications like security cameras and robotics. It is becoming critical for edge devices to have embedded AI inferences for real-time actions and critical decisions. AI services provided exclusively via cloud-based inference will not suffice because it requires a low latency network infrastructure for bi-directional data transmission. In addition, concerns about privacy, latency, bandwidth and costs are some of the drivers pushing the adoption of AI processing at the edge.

Currently, most AI services offered today are cloud-based. However, this is changing. Network latency is the time it takes to transfer data between the cloud and edge device is becoming a concern because users expect real-time responses to their data requests.

AI at the edge is the preferred approach because it has lower bandwidth requirements. Bandwidth is the volume of data transferred in a given time. Computer vision, facial and object recognition related applications may require more bandwidth. The reason is that the resolution at which images and videos are captured and transferred determines the bandwidth needed.

Cloud-based AI processing is vulnerable to cyber security attacks, but edge-based processing can decrease cyber security vulnerabilities. An AI powered-edge device that has scheduled synchronization with an associated server-side system can detect anomalies in data communications and intrusion attacks faster than a purely cloud-based solution.

In the long run, edge-based processing has a higher ROI than cloud-based processing. Two of the main drivers are the edge hardware’s competitive pricing and the reduction or the complete elimination of bandwidth costs for transmitting data between devices at the edge and the cloud.

About the author

Sheldon Gabriel is the founder and CTO of Ai-tronics Systems. Ai-tronics specializes in physical security, building automation integration, and video management system hardware and software solutions based on AI, machine learning and IoT.