AI-Edge-Devices
AI-Edge-Devices
AI Edge Devices

AI Edge Devices- Change has always been integral to development. With fast-evolving technologies, companies, too, need themselves to embrace these for maximized benefits. Artificial Intelligence (AI) moving to edge IoT devices and networks, just like we witnessed computing switch from mainframes to the cloud. And as data continues to grow, we need to opt for data storage and data computation to be located on the device. Companies like Qualcomm, NVIDIA, and Intel are helping us achieve this reality.

While edge site computing systems are much smaller than those found in central data centers, they have matured, and now successfully run many workloads due to immense growth in the processing power of today’s x86 commodity servers. Plus, edge is a better option if an application is latency-sensitive. Better privacy, Security, low latency, and bandwidth are some of the hallmarks of edge platform.

But What is Edge AI?

It refers to AI algorithms that are processed locally on a hardware device. It is also referred to as on-Device AI. This allows you to process data with the device in less than a few milliseconds, which gives you real-time information. Using Edge AI, one can get personalization features that she wants from the app on the device.

According to IDC, the Edge AI Software market is forecasted to grow from $355 million in 2018 to 1.12 trillion dollars by 2023. Dave McCarthy, research director, IDC, says, “AI is the most common workload in edge computing. As IoT implementations have matured, there has been an increased interest in applying AI at the point of generation for real-time event detection.”

Edge over Cloud

Currently, AI processing is done with deep learning models in a cloud-based data center that require massive computing capacity. And latency is one of the most common issues faced in a cloud environment or IoT devices backed by the cloud. Besides, there is always a risk of data theft or leak during data transfer to the cloud. With edge, data is curated before sending it off to a remote location for further analysis. Further, edge AI shall enable intelligent IoT management.

In edge-based architecture, inference happens locally on a device. This decreases the amount of network traffic flowing back to the cloud with the response time for IoT devices cut to a minimum, thus enabling management decisions to be available on-premise, close to the devices offering numerous advantages.

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Article Credit: Analytics Insight

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