Previous Blogs

October 15, 2019
Poly Extends Collaboration Options

October 8, 2019
Arm Extends Reach in IoT

October 1, 2019
A 5G Status Report

September 24, 2019
Revised Galaxy Fold Adds New Twist to Fall Phone-a-Palooza

September 3, 2019
Huddle Rooms and Videoconferencing Reshaping Modern Work Environments

August 27, 2019
VMware Paints Multi-Faceted Picture of Computing Future

August 20, 2019
Server Chips Now Leading Semiconductor Innovations

August 13, 2019
Samsung and Microsoft Partnership Highlights Blended Device World

August 6, 2019
IBM Leveraging Red Hat for Hybrid Multi Cloud Strategy

July 30, 2019
T-Mobile, Sprint and Dish: It’s All about 5G

July 23, 2019
The Contradictory State of AI

July 16, 2019
Changes to Arm Licensing Model Add Flexibility for IoT

July 9, 2019
Intel Highlights Chiplet Advances

July 2, 2019
Ray Tracing Momentum Builds with Nvidia Launch

June 25, 2019
AT&T Shape Event Highlights 5G Promise and Perils

June 18, 2019
HPE and Google Cloud Expand Hybrid Options

June 11, 2019
AMD's Gamble Now Paying Off

June 4, 2019
Apple Blurs Lines Across Devices

May 21, 2019
Citrix Advances the Intelligent Workspace

May 14, 2019
Next Major Step in AI: On-Device Google Assistant

May 7, 2019
Microsoft Bot Frameworks Enable Custom Voice Assistants

May 1, 2019
Dell Technologies Pushes Toward Hybrid Cloud

April 23, 2019
Intel and Nvidia Partner to Drive Mobile PC Gaming

April 16, 2019
Samsung Galaxy Fold Unfolds the Future

April 9, 2019
Google Embraces Multi-Cloud Strategy with Anthos

April 8, 2019
Intel Helps Drive Data Center Advancements

April 2, 2019
Gaming Content Ecosystem Drives More Usage

March 26, 2019
PCs and Smartphones Duke it Out for Gaming Champion

March 19, 2019
PCs and Smartphones Duke it Out for Gaming Champion

March 12, 2019
Proposed Nvidia Purchase and CXL Standard Point to Data Center Evolution

March 5, 2019
Tech Standards Still Making Slow but Steady Progress with USB4 and WebAuthn

February 26, 2019
Second Gen HoloLens Provides Insights into Edge Computing Models

February 19, 2019
IBM’s Watson Anywhere Highlights Reality of a Multi-Cloud World

February 12, 2019
Extending Digital Personas Across Devices

February 5, 2019
Could Embedded 5G/LTE Kill WiFi?

January 29, 2019
Successful IT Projects More Dependent on Culture Than Technology

January 22, 2019
XR Gaming Market Remains Challenging

January 15, 2019
The Voice Assistant War: What If Nobody Wins?

January 8, 2019
Big CES Announcements are TVs and PCs

January 2, 2019
Top Tech Predictions for 2019

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TECHnalysis Research Blog

October 22, 2019
Nvidia EGX Brings GPU Powered AI and 5G to the Edge

By Bob O'Donnell

The concept of putting more computing power closer to where applications are occurring, commonly referred as “edge computing”, has been talked about for a long time. After all, it makes logical sense to put resources nearer to where they’re actually needed. Plus, as people have come to recognize that not everything can or should be run in hyperscale cloud data centers, there has been increasing interest in diversifying both the type and location of the computing capabilities necessary to run cloud-based applications and services.

However, the choices for computing engines on the edge have been somewhat limited until now. That’s why Nvidia’s announcement (well, technically, re-announcement after its official debut at Computex earlier this year) of its EGX edge computing hardware and software platform has important implications across several different industries. At a basic level, EGX essentially brings GPUs to the edge, allowing IoT, telco, and other industry-specific applications, not typically thought of as being Nvidia clients, the ability to tap into general purpose GPU computing.

Specifically, the company’s news from the MWC LA show provides ways to run AI applications fed by IoT sensors on the edge, as well as two different capabilities important for 5G networks: software-defined radio access networks (RANs) and virtual network functions that will be at the heart of network slicing features expected in forthcoming 5G standalone networks.

Nvidia’s announced partnership with Microsoft to have the new EGX platform work with Microsoft’s Azure IoT platform is an important extension of the overall AI and IoT strategies for both companies. Nvidia, for example, has been talking about doing AI applications inside data centers for several years now, but until now they haven’t been part of most discussions for extending AI inferencing workloads to the edge in applications like retail, manufacturing, and smart cities. Conversely, much of Microsoft’s Azure IoT work has been focused on much lower power (and lower performance level) compute engines, limiting the range of applications for which they can be used. With this partnership, however, each company can leverage the strengths of the other to enable a wider range of distributed computing applications. In addition, it allows software developers a consistent platform from large data centers to the edge, which should ease the ongoing challenge of writing distributed applications that can smartly leverage different computing resources in different locations.

On the 5G side, Nvidia announced a new liaison with Ericsson—a key 5G infrastructure provider—which opens up a number of interesting possibilities for the future of GPUs inside critical mobile networking components. Specifically, the companies are working out how to leverage GPUs to build completely virtualized and software-defined RANs, which provide the key connectivity capabilities for 5G and other mobile networks. For most of their history, cellular network infrastructure components have primarily been specialized, closed systems typically based on custom ASICs, so the move to support GPUs potentially provides more flexibility, as well as smaller, more efficient equipment.

For the other 5G applications, Nvidia partnered with RedHat and its OpenShift platform to create a software toolkit they call Aerial. Leveraging the software components of Aerial, GPUs can be used to perform not just radio access network workloads (which should be able to run on the forthcoming Ericsson hardware), but virtual network functions behind 5G network slicing. The concept behind network slicing is to deliver individualized features to each person on a 5G network, including capabilities like AI and VR. Network slicing is a noble goal that’s part of the 5G standalone network standard but will require serious infrastructure horsepower to realistically deliver. In order to make the process of creating these specialized functions easier for developers, Nvidia is delivering containerized versions of GPU computing and management resources, all of which can plug into a modern, cloud-native, Kubernetes-driven software environment as part of RedHat’s OpenShift.

Another key part of enabling these network slicing capabilities is being able to process the data as quickly and efficiently as possible. In the real-time environment of wireless networks, that requires extremely fast connections to data on the networks and the need to keep that data in memory the whole time. That’s where Nvidia’s new Mellanox connection comes in, because another key function of the Aerial SDK is a low-latency connection between Mellanox networking cards and GPU memory. In addition, Aerial incorporates a special signal processing function that’s optimized for the real-time requirements of RAN applications.

What’s also interesting about these announcements is that they highlight how far the range of capabilities has expanded with GPUs. Well past the early days of faster graphics in PCs, GPUs, included as part of the EGX offering, now have the software support to be relevant in a surprisingly broad range of industries and applications.

Here's a link to the column:

Bob O’Donnell is the president and chief analyst of TECHnalysis Research, LLC a market research firm that provides strategic consulting and market research services to the technology industry and professional financial community. You can follow him on Twitter @bobodtech.

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