Making the Way for Speeding 5G and IoT by Advances in AI

Advances in AI: Making the Way for Speeding 5G and IoT

AcceleraConnected gadgets connected to the network technology 5G and IoT Internet of Things are increasingly widespread. But wait until next-generation applications like artificial intelligence (AI) begin to run on these edge devices. The low latency and faster data throughput of 5G and IoT will give AI a new real-time dimension.

Imagine an extended reality (XR) headset that not only displays a 3D image of the inside of an airplane’s engine but also has onboard intelligence to guide you to issues areas.

Chipmakers are already working on strong yet energy-efficient processors – known as “systems on a chip” – that can enable AI computing in a small-footprint device. Qualcomm, for example, just launched AI-capable Snapdragon CPUs for smartphones and PCs. A new generation of NeuRRAM chips, created at the University of California, San Diego, is also on the horizon, capable of executing large AI algorithms on smaller devices.

According to a new zScaler estimate, the global number of linked IoT devices is expected to exceed 29 billion by 2027, which is more than 16.7 billion at the moment. Consumer devices are the smartest and most common, but commercial process-oriented IoT produced the most transactions, write the report’s authors. Manufacturing and retail gadgets accounted for more than half of all transactions, demonstrating their widespread adoption and critical role in these industries. The most unencrypted transactions are generated by enterprise, house automation, and entertainment devices.

Centralized environments, such as the cloud and data centers, may provide computational capacity for fully immersive experiences, but they may be too far away from where low-latency resources are located.

In order to take benefit of the ultra-low latency that is one of the primary advantages of 5G, computing near the edge plays an important part by providing smaller amounts of data storage and processing much closer to the device where it is required. Edge computing can be adapted to specific use cases, such as storing content for based-demand video transmission or running AI algorithms for quick decision-making.


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