The “edge” has become a popular term because it brings value to businesses. Edge computing refers to the data at the network’s edge – on, near or around the physical thing producing the data, which allows the local devices to process the time-sensitive data, rather than having to send the data to a centralized control server for analysis.
Edge Computing Innovation
Edge computing is making it more efficient and cost-effective at turning raw data into valuable control data that is actionable, which is critical for machines to become more automated, and more autonomous. It is the key factor in the efficiency and effectiveness of a high-demanding AI and IoT system (AIoT). It further improves latency and reduces costs as it removes points of failure from critical decision-making and operations.
Where does Artificial Intelligence come in?
Artificial Intelligence (AI), which was originally targeted at data centers and cloud, is moving towards the edge of the network, where it is needed to make fast and critical decisions locally and closer to the masses. In applications such as autonomous driving, it is rapidly more important that the time-sensitive data (detecting a coming car or pedestrian) is provided closer to the driver aspect, enabling speedier decision-making and quick actions.
Why does Edge AI Inferencing Matter?
The ability to bring AI inferencing closer to the end user, enables designers to incorporate AI into a wider range of affordable products and applications. A few examples are edge servers, high accuracy/quality imaging, voice applications with lower throughput inference, and smart phone applications. Edge servers deployed in factories, hospitals, retail and financial enterprises can be imbued with AI inference capabilities to help manage inventories, analyze consumer behaviors and even predict defects before they happen. Robotics, industrial automation, medical and scientific imaging applications demand high volume, high accuracy quality imaging, which can be captured fast and efficiently through edge AI inference capabilities. Voice processing applications and smart phone applications requires millisecond recognition and responses can take advantage of edge AI inference accelerators.
Edge AI Inference Critical Functionalities
A large percentage of industrial and business data is not processed because by the time data would normally be processed to drive an outcome, the data would no longer be relevant. Edge AI can change all that.
Bringing AI to the networks edge allows for fast AI computing throughput, high volume efficiency, and reduction in network latency and associated costs. Edge AI also allows data to remain local, further increasing privacy and security.
Edge AI Inference Advances
AI inferences at the edge is opening up vast amounts of new markets and innovative applications that can benefit from its high throughput, efficiency, and accuracy. The industry is just beginning to realize edge computing’s potential to create faster, more reliable, and cost-effective systems, bringing more value for businesses.
Lanner Edge AI Platforms
Edge AI computing uses embedded computers, integrated hardware/software systems and modules to allow the data to be extracted, computed and shared at the source.
Lanner’s LEC-2290E is a robust GPU intelligent edge computing appliance powered by the Intel® CoreTM i7-8700 (codenamed Coffee Lake S) processor. This validated NGC-ready server for Edge computing, configured with NVIDIA® T4 GPUs, can be trusted for real-time intelligent decision making, leveraging hyper-converged AI-enabled application deployments at the edge, suitable for the realization of Edge AI for applications in 5G Open RAN, edge data centers, private networks, and MEC.
Nvidia NGC-ready Edge AI Appliance With NVIDIA® T4 GPU Compatibility
|CPU||Intel® Core™ i7-8700 (Codenamed Coffee Lake S)|