How 5G Will Serve AI and Vice Versa Posted on : Dec 11 - 2019

5G is the future of the edge. Though it’s still several years away from widespread deployment, 5G is a key component in the evolution of cloud-computing ecosystems toward more distributed environments. Between now and 2025, the networking industry will invest about $1 trillion worldwide on 5G, supporting rapid global adoption of mobile, edge, and embedded devices in practically every sphere of our lives.

5G will be a prime catalyst for the trend under which more workloads are executed and data resides on edge devices. It will be a proving ground for next-generation artificial intelligence (AI), offering an environment within which data-driven algorithms will guide every cloud-centric process, device, and experience. Just as significant, AI will be a key component in ensuring that 5G networks are optimized from end to end, 24×7.

How 5G Will Serve AI

AI will live at every edge in the hybrid clouds, multiclouds, and mesh networks of the future.  Already, we see prominent AI platform vendors—such as NVIDIA—making significant investments in 5G-based services for mobility, Internet of Things (IoT) and other edge environments.

To better understand how 5G will superpower the online economy, let’s consider how this emerging wireless architecture will deliver value throughout the AI toolchain:

Next-generation edge convergence with AI systems on chip: 5G converges digital cellular technology with wireless Long-Term Evolution and Wi-Fi interfaces. When implemented in cross-technology network interfaces, 5G will enable every edge device to seamlessly roam between indoor and wide-area environments. The technology’s adoption may someday lead to convergence of the radio spectra for these disparate radio channels and convergence of network interfaces down to single chips that are agile at maintaining seamless connections across multiple radio access technologies. These same 5G interfaces will undoubtedly be converged with neural network processing circuitry into low-power, low-cost systems on chip for many mass-market AI apps. View More