Change has always been integral to development. With the emerging technologies, companies, too, need themselves to embrace these for maximized benefits. AI is moving to edge IoT devices and networks. 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 to achieve this reality.
But What is Edge AI?
Edge AI 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.
Edge over Cloud
AI processing is done with deep learning models in a cloud-based data center that require large computing capacity. And latency is one of the most common problems faced in a cloud environment or IoT devices backed by the cloud. Besides, there is always a problem of data theft or leak during data transfer to the cloud. With the edge, data is arranged before sending it off to a remote location for further analysis. Further, edge AI shall enable intelligent IoT management.
Drivers Of Edge AI demand:
There are many factors that demand moving AI processing to the edge:
• Real-time customer engagement is irrespective of the device or user location.
• Ability to run large-scale DNN models on the edge devices.
• Analysis and quick processing of IoT sensor data.
• Lower bandwidth costs of Edge platforms.
Edge Device Products
Depending on the AI application and device category, there are many hardware options for performing AI edge processing. The options involve central processing units (CPUs), GPUs, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASICs), and system-on-a-chip (SoC) accelerators. The edge, for the most part, refers to the device and does not include microdata centers or network hubs, except in the case of security cameras where network video recorders (NVRs) are involved.