Author: Bob O'Donnell

For a company that doesn’t manufacture anything, Arm has a surprisingly large and broad impact, not only on the chip industry, but the overall tech industry and, increasingly, many other vertical industries as well.

The company—which creates semiconductor chip designs that it licenses as IP (intellectual property) and then Arm’s customers use the designs to build chips—is the brains behind virtually every smartphone ever made. In addition, it has a small but growing market in data center and other network infrastructure equipment and is the long-time leader in intelligent devices of various types—from toys to cars and nearly everything in between; essentially the “things” part of IoT (Internet of Things).

As a result, it’s not terribly surprising to see the company pushing ahead on new innovations to power more devices. What is unexpected about Arm’s latest announcements, however, is the degree of performance that it’s enabling in microcontrollers—tiny chips that power billions of devices. Specifically, with the launch of its new Cortex-M55 Processor, companion Ethos-U55 microNPU (Neural Processing Unit) accelerator, and new machine learning (ML) software, Arm is promising a staggering 480x improvement in ML performance for a huge range of low-power applications. That’s not the kind of performance numbers you typically hear about in the semiconductor industry these days. Practically speaking, it turns something that was nearly impossible into some very doable.

More importantly, because microcontrollers are the tiny, unsung heroes powering everything from connected toothbrushes to industrial equipment, the potential long-term impact could be huge. Most notably, the addition of AI intelligence to all these types of “things” offers the promise of finally getting the kind of smart devices that many hoped for in areas from smart home to factory automation and beyond. Imagine adding voice control to the smallest of devices or being able to get advanced warnings about potential part failures in slightly larger edge computing equipment through onboard predictive maintenance algorithms. The possibilities really are quite limitless.

The announcements are part of an overall strategy at Arm to bring AI capabilities to its full range of IP designs. Key to that is work the company is doing on software and development tools. Because Cortex-M series designs have been around for a long time, there’s a large base of applications that device designers can use to program them. However, because a great deal of ML and AI-based algorithm work is being created in frameworks, such as TensorFlow, the company is also bringing support for its new IP designs into TensorFlow Lite Micro, which is optimized for the types of smaller devices for which these new chips are intended.

In addition to software, there are several different hardware-centric capabilities that are worth calling out. First, the Cortex-M55 is the first microcontroller design to incorporate support for the company’s Helium vector process technology, previously only found on larger Arm CPU cores. The M55 also includes support for Arm Custom Instructions, an important new capability that lets chips designers create custom functions that can be optimized for specific workloads.

The new Ethos-U55 is a first of its kind dedicated AI accelerator architecture that was designed to pair with the M55 for devices in which the 15x improvement in ML performance that the M55’s new design offers is not enough. In addition, the combination of the M55 and the U55 was specifically intended to offer a balance of scalar, vector, and matrix processing, which is essential to efficiently running a wide range of machine-learning-based workloads.

Of course, there are quite a few steps between releasing new chip IP designs and seeing products that leverage these capabilities. Unfortunately, that means it will likely be sometime near the end of 2021 and into 2022 before we can really see the promised benefits of a nearly 500x improvement in machine learning performance. Plus, it remains to be seen how challenging it will be for low-power device designers to create the kinds of ML algorithms they’ll need to make their devices truly smart. Hopefully, we’ll see a large library of algorithms developed so that device designers with little to no previous AI programming experience can leverage them.

Ultimately, the promise of bringing machine learning to small, battery-powered devices is an intriguing one that opens up some very interesting possibilities for the future. It will be interesting to see how the “things” develop.

It’s not often you find something that people do just for fun to be tremendously impactful from both a revenue and technology perspective, but that’s exactly the case for the many options that people are going to have for playing games over the internet.

In fact, cloud-based gaming is expected to be one of the most important trends of the new decade. Not only does it offer the potential to drive large amounts of income for many different companies, the technical demands required are going to have a big impact across a number of different areas. Everything from cloud-based computing services, to 5G and other wireless network infrastructure, to individual device components and architectures are now being optimized to enable high-quality, cloud-based gaming services.

It’s not hard to see why. Gaming has become a huge global phenomenon, and interest in gaming across multiple devices has grown tremendously—a point that my 2019 study on Multi-Device Gaming made abundantly clear (see “PCs and Smartphones Duke it Out for Gaming Champion” for more). As a result, companies are eager to create solutions that can tap into the enormous interest in gaming in a way that gives consumers more flexibility (and better performance) than they’ve had before.

Not surprisingly, graphics chip leader Nvidia has been involved with several of these efforts, but none as directly as its own GeForce Now game streaming service, which just became generally available across the US and several other nations around the world starting today. The basic idea with GeForce Now—which has been in a private beta period for several years—is that it enables people to play high-quality, graphically intensive PC games across a range of different devices including PCs, Macs, Android Phones, Nvidia’s own Shield device, and certain smart TVs via an internet connection. (Support for Chrome-based devices is expected later this year.) Importantly, the games are running on cloud-based servers in Nvidia’s (or a few regional partners’) own dedicated data centers and are powered by the company’s high-end GeForce GPUs. As a result, the quality of the gaming experience is nearly what you’d expect it to be on one of today’s best dedicated gaming PCs. However, you can achieve that quality consistently on any of the different device types—even on older devices without any dedicated graphics acceleration hardware. Plus, you have the ability to start a game on one device and then pick up where you left off on another one, a capability that my previously mentioned research suggests is eagerly prized by many gamers.

Technologically, Nvidia is leveraging its ability to virtualize GPUs in its data centers and is using a variety of compression techniques and screen-sharing protocols to deliver remote access to its super-powered cloud-based computers. One nice improvement that the company is bringing to the GeForce Now service with its public launch is the ability to support its RTX real-time ray tracing technology (in games that use it). Until now, that capability has only been found in their highest end graphics cards, like the RTX2080, so this should bring it to a much wider audience.

Nvidia is taking an interesting, and different, approach to the games available on the GeForce Now platform than some of the other cloud-based game services that have been announced. Because it’s actually running PC games on PC hardware, it allows customers of the service to play their existing library of PC games—they simply have to provide proof of ownership of the title and they can access it via their GeForce account. In addition, there are hundreds of free-to-play games, and consumers can use their existing PC game store accounts. Also, because it’s all being stored and run in the cloud, game patches and driver updates (two common banes of PC gamers’ existence) are taken care of automatically, without any interventions on the user’s part. In other words, Nvidia is trying to make the process of using the service as seamless as possible for both casual and hardcore PC gamers.

From a pricing perspective, the company is providing two options with its public launch. You can have an unlimited number of up to 1 hour gaming sessions for free, or you can sign-up for the $4.99/month Founders account (the first three months are free), which gives you priority access to the service, lets you have up to 6-hour sessions, and turns on the RTX ray-tracing support.

In some ways, you could argue that GeForce Now is a bit of a risky business proposition for Nvidia, because, if enough consumers find the service to be sufficient for their needs, they could end up buying less dedicated gaming hardware. Plus, given the high cost of building out and maintaining the data centers necessary to run GeForce Now, especially in comparison to its very low pricing, it seems like profitability could be a challenge—at least initially. Ultimately, though, Nvidia seems confident that GeForce Now won’t replace dedicated gaming PCs for hard-core gamers and could even entice more casual gamers to better appreciate what high-quality PC gaming experiences can enable, which may in turn get them to purchase their own dedicated PC gaming rigs as well. If that proves to be the case, it could end up being a nice bit of incremental revenue as well as a technological showcase for what the best of PC gaming can offer.

One of the biggest misconceptions about cloud computing is that companies pick a single cloud computing platform and then stick with it for all their cloud computing efforts. As new research from TECHnalysis Research points out, today’s businesses are using a multiplicity of cloud providers and cloud types, and are putting different workloads in different places, often for different reasons.

In last week’s column (see “New Research Shows It’s a Hybrid and Multi-Cloud World”), I described the overall purpose and approach for the new Hybrid and Multi-Cloud Study. Also mentioned was the overall diversity of cloud computing efforts, including the fact that companies are using an average of 3.1 different public cloud providers across IaaS (Infrastructure as a Service) and PaaS (Platform as a Service) applications, and that usage of private and hybrid cloud environments is very strong. This week, I’ll dive a bit deeper into the types of workloads organizations are running in the cloud, where they’re running them, and why.

First, it’s interesting to get a perspective on what types of applications companies have moved to the cloud. As Fig. 1 illustrates, there is a wide variety of different applications that are being run in the cloud, most of which are being used by more than half of all the survey respondents. (Note that Figure 1 provides an overall view of cloud-based workloads across public, private, and hybrid clouds.)

Fig. 1

Not surprisingly, Databases are the most common workloads being used overall, and they also happen to be the top choice within public, private, and hybrid cloud installations individually. Of course, the types of Databases and their specific function can vary quite a bit, but it’s clear that businesses have become very comfortable running them in cloud environments. Analytics-based workloads were the second most popular overall, followed closely by Web or Content Hosting. While details can vary by company, many of the Analytics-focused workloads are likely new variations on Big Data efforts that have been at the core of enterprise computing for most of the last decade. Web/Content Hosting is, of course, ideally suited to a cloud-based environment and likely one of the first that many organizations chose to move to the cloud.

Interestingly, when you break down the workload types by just Public Cloud platforms, the order of the two are reversed, with Web/Content Hosting being second and Analytics-focused efforts being third, emphasizing the importance that web hosting plays in the Public Cloud. The story is much different when you break down the top workloads for Private/Hybrid Cloud environments, however. There vertical-specific Industry Market Solutions are second after Databases, followed by workloads focused on Legacy/App Migration to Containers. Both make perfect sense for the types of applications that businesses want to keep a bit more private as well as allow their internal development teams to create new company-specific software solutions.

The diversity in cloud computing choices extends to specific Public Cloud Computing providers as well. It turns out companies are selecting different providers for different workloads, as the data in Table 1 illustrates. Specifically, it lists the top 5 workloads that survey respondents said they were running at each of the top 3 public cloud platforms: Microsoft’s Azure, Amazon’s AWS, and Google’s GCP.

Table 1

As you can see, while there is some degree of commonality, there’s also a surprisingly large amount of differences, with Databases being the only workload that made the top 5 of all three providers. What’s interesting is that the top workloads in each platform reflect a bit of each company’s heritage in the cloud and overall. In Azure, for example, Microsoft’s heritage of database strength with its SQL Server platform clearly has an influence, while the strong tie between many vertical-specific applications (which are incorporated in Industry Market Solutions) and Windows also undoubtedly played a role. As the early leader in cloud computing environments, Amazon is the logical choice for many Web/Contest Hosting platforms, while its early PaaS efforts focused on data analysis capabilities. Finally, given Google’s heritage as an overall cloud innovator that built services based on large databases and is generally credited for developing many of the cloud-native software development tools and processes, the strength of Database and Software Development/DevOps workloads make sense as well.

Cloud-based computing is clearly continuing to take on an increasingly important role for companies of all sizes and, as this research illustrates, the range of cloud types and platforms now being used gives organizations a wealth of choices in determining how to spread out their various workloads. As with virtually anything related to enterprise computing, those choices can quickly become a bit overwhelming. At least now organizations can see that there aren’t necessarily wrong choices when it comes to the cloud, but rather, a host of options that they can consider.

(You can download a free copy, in PDF format, of the highlights of the TECHnalysis Research Hybrid and Multi-Cloud Study here. The full version 123-slide version is available for purchase.)

It’s getting to be a familiar theme. As with last year’s event, some of the most interesting announcements from this year’s CES in Las Vegas are focused around PCs. In fact, this year, there are probably more PC developments from a wider variety of vendors than we’ve seen in quite some time. From foldable displays, to 5G, to AI silicon, to sustainable manufacturing, the latest crop of PCs highlights that the category isn’t just far from dead, it’s actually at the cutting edge of everything that’s expected to be a hot topic for this new decade.

On top of that, some of the most important advancements in PC-focused CPUs in a long time have also been announced at the show, promising big leaps in bread-and-butter performance metrics for the coming year as well. In short, it’s a real PC renaissance.

Probably the flashiest new PC from CES is technically one that’s already been hinted at before, but whose final details were just released at the show: Lenovo’s ThinkPad X1 Fold. Leveraging a plastic OLED display from LG Display (similar in concept to what’s used on foldable phones like the Samsung Galaxy Fold and Motorola Razr), the X1 Fold shrinks a 13.3” screen down to a small leather-wrapped portfolio size when it’s folded in half. Unlike the phone displays, however, the X1 Fold supports pen input from the included active stylus.

In addition, the Intel Hybrid Technology (formerly “Lakefield”)-powered X1 Fold supports several different modes of operation, including a completely unfolded tablet-style mode, and a partially folded traditional notebook style, which gives you the option to either use a soft keyboard or treat the display as two separate screens. Importantly, the $2,499 device includes a magnetic Bluetooth keyboard that functions as you would expect but can also be stored and charged inside the X1 Fold when it’s folded. That’s critical for the many people who have had challenges with (or simply stayed away from) early experiments with dual-screened notebooks. In addition, Lenovo plans to offer optional 5G support. The first version of the X1 Fold is expected mid-year and will run Windows 10, but the company also plans to offer the ability to run Windows 10 X (the forthcoming dual-screen and foldable-optimized version of the OS that Microsoft announced when it previewed its Surface Neo foldable device) later this year.

In conjunction with Qualcomm, Lenovo also showed what they claimed was the world’s first 5G PC, the $1,499 Yoga PC. The new notebook is an Arm-based Qualcomm 8cx-powered device that—somewhat surprisingly—supports both sub-6 and mmWave variations of 5G, thanks to some advanced antenna development work by Lenovo.

HP had a number of interesting new announcements at this year’s show, including an update to its super light-weight 13.3” DragonFly notebook, which features an integrated sub-6 GHz 5G modem (from Qualcomm), as well as another version that offers a built-in Tile device, for easily locating the notebook in the event it’s lost or stolen. (Unfortunately, however, both options use the M.2 slot, so they don’t have one that offers both yet.) The Intel-based DragonFly Elite G2 supports an optional 4K HDR display and an optional integrated privacy screen via the company’s Sure View Protect that prevents the screen from being read at an angle. Even more importantly, several components of the DragonFly are built from recycled materials, including the speaker enclosure, which is made from 100% ocean-bound plastics, and the chassis, which is 90% recycled magnesium.

For content creators and gamers, the company also debuted the first all-in-one desktop system featuring Nvidia’s RTX technology for real-time ray-tracing support. The HP Envy 32 AIO features a 31.5” 4K HDR-enabled display, the Nvidia RTX 2060 GPU, 9th generation Intel Core CPU and a Bang & Olufsen designed audio subsystem for a robust multimedia experience.

Dell showed off an updated version of its groundbreaking XPS13 that now extends its nearly bezel-less Infinity Display to all four sides, as well as a number of very cool-looking concept PCs, including its own foldable design and a gaming-focused device. In addition, Dell’s new Latitude 9750 2-in-1 is a 15-inch device weighing 3.2 pounds that features integrated sub-6 GHz 5G. The 9750 also leverages a number of AI-based features designed to subtly improve the performance and battery life behind the scenes thanks to some new Intel-developed software.

On the gaming side, Dell also unveiled the new $799 G5 SE notebook, which leverages AMD’s latest mobile CPUs and GPUs as well as its new SmartShift technology. Essentially Smart Shift allows the discrete CPU and GPU to function more like an integrated APU, thereby improving performance and increasing battery life.

Samsung is also kicking its PC and related peripherals business into higher gear with the official debut of the $849 Galaxy Book Flex α, the latest in its line of thin, QLED display-equipped 2-in-1 notebooks, as well as new gaming-specific monitors. (QLED technology is the same that the company uses in their current high-end TVs, including their new 95” 8K model, the Q950TS. On notebooks, QLED delivers brighter displays and, according to the company, longer battery life.) The Galaxy Book Flex α is 2.26-pound, Intel-based, pen-equipped device that, somewhat confusingly, is in addition to the already announced (and more powerfully spec’d) Galaxy Book Flex 2-in-1, which was announced last fall, but has yet to start shipping in the US. The company also introduced one of the first Intel Project Athena-verified Chromebooks, the Galaxy Chromebook, including one in a slick-looking red color.

For gaming monitors, Samsung is also leveraging QLED technology but in a 100° curved format that’s designed to match the peripheral vision range of the human eye. Available in both a 49” version with 32:9 Dual Quad HD resolution (that’s 5,120 x 1,440)—the G9, or 32”/27” versions with 16:9 Quad HD resolution (2,560 x 1,440)—the G7, both lines of monitors feature 240 MHz refresh rates, response times of 1 msec, and support for both Nvidia’s G-Sync and AMD’s FreeSync technologies.
Speaking of which, AMD and Intel both announced their latest generation CPU architectures at CES. Additionally, while Qualcomm debuted its latest PC CPUs last month, it made a point to say at their press conference that its biggest news for this year’s CES was in PCs (in part because of the Lenovo 5G PC mentioned earlier).

In AMD’s case, the company debuted the first mobile parts based on its Zen2 core, the Ryzen 4000 series in three different variations: ultrathin, gaming, and high-performance. In the desktop world, AMD’s 7nm Zen2 core-powered desktop CPUs have surpassed Intel in performance for the first time in about 20 years, so many people have been waiting for these mobile versions and early benchmarks provided by the company looked impressive. In fact, for the Ryzen 4800H version, which is a 45W mobile part, AMD showed it outperforming Intel’s top-end 95W desktop part.

Speaking of desktop, AMD also extended its Threadripper CPU line with the Ryzen Threadripper 3990 (also priced at $3,990) that offers a staggering 64 cores and 128 independent threads for performance on ultra high-end and demanding applications, such as editing 8K video. It’s clearly not for everyone but demonstrates the impressive levels of performance that AMD has been able to achieve.

In Intel’s case, the company formally unveiled its Tiger Lake CPU line, based on its 10nm+ process technology and, more importantly, a new CPU design. One of the most interesting bits of news about Tiger Lake is that it incorporates a new integrated graphics solution called Xe that’s based on the work the company has been doing on its upcoming, first-ever standalone GPU, codenamed DG1 (which was also demo’d at their press conference). Intel is claiming speed improvements across all aspects of its architecture with Tiger Lake, with a particularly large boost in AI processing. Up until now there’s been little focus on AI-specific tasks on PCs—particularly compared to smartphones—so it’s good to see the company highlighting that development for PCs. Finally, Intel also showed off a new prototype foldable PC design, codenamed “Horseshoe Bend”, featuring a future version of Tiger Lake that folds out into a 17” touchscreen display. Intel also discussed extending its Project Athena PC experience spec for a new line of foldable devices that the company expects to see this year and beyond.

In all, it was an impressive showing for a product category that many predicted would barely even make it into this decade. Based on the news from this year’s CES, it’s probably a safe bet that we’ll be talking about PCs as we enter the 2030s as well.

The future of just about everything tech-related right now, or so it seems, revolves around designing custom semiconductor chips. From smartphone makers like Apple and Samsung, to cloud computing providers like Amazon and Google, stretching even to automakers like Tesla, there’s been an enormous amount of effort among tech vendors recently to create their own specialized silicon parts.

The most recent example comes from networking powerhouse Cisco, which just unveiled a new silicon platform last week, called Silicon One, that they believe is necessary to power the next generation internet, as well as the infrastructure necessary to support 5G networks. Based on current growth rates and predicted demand, the amount of data traffic that each of these elements will demand (as well as the obvious tie-overs between them), will completely overwhelm the existing networking infrastructure. Plus, the economics of trying to scale these efforts with existing devices paints an even bleaker future—hence the need to take a radically different approach to networking gear.

To be completely accurate, Cisco has been designing and building networking-specific chips for over two decades. What’s different and significant about Silicon One is that, in addition to using it themselves, the company also plans to sell this chip to service providers and other potential partners who may want to build their own devices. That’s quite a change from a company that was never seen as a silicon supplier.

The other unique thing about the Silicon One platform is the design and functionality of the architecture. Cisco claims that they’ve been working on it for 5 years and started with a clean sheet of paper. When you look at what it’s designed to do, it’s easy to see why. Rather than pursuing enhancements to existing types of traditional routing and switching chips, they decided to create a more programmable structure that would allow a single chip family to perform a variety of different networking-related functions for different applications, including backhaul, core, edge and more. The goal was to achieve new levels of performance—over 10.8 terabits/sec—in a single rack space unit using the Q100 chip and to make the traditionally slower routing chips as fast as those that do switching.

Silicon One achieves this with an architecture that, at first glance, sounds somewhat like an FPGA (field-programmable gate array), which is a completely programmable set of circuits embedded into these chips that are often used in networking devices. Further conversations with Cisco representatives at their launch event in San Francisco last week clarified, however, that the Q100, which is the first specific iteration of the Silicon One family, isn’t an FPGA, but rather, a different type of ASIC (application-specific integrated circuit) design. The Silicon One family of chips (others are still to come) integrates multiple types of optimized networking functions within its design that can be turned on or off with a software API (application programming interface). This allows equipment builders to basically turn on and off various sets of functionalities as needed, depending on the specific tasks the device is intended to do.

So, for example, if either Cisco or one of its silicon customers wants to build a device that’s primarily dedicated to high-speed routing, they can enable those functions, whereas someone else building a piece of infrastructure equipment that needs more switching capabilities can turn those on—both with the same chip. According to Cisco, turning on or off certain functions doesn’t change the performance of the chip. The goal was simply to create a single silicon platform that could be more easily used and have software written for, across multiple different types of networking functions, thereby saving the capital costs involved in designing and testing multiple types of systems on multiple different chip architectures.

Speaking of software, the company also unveiled a new version of the operating system they use inside their devices called IOS XR7 (no, not that iOS) that’s been optimized to work with the new silicon. XR7 will work on the new line of 8000-series devices—the first to feature Silicon One-based chips—as well as previous generations going back to the NCS 540, 560, and 5600 lines. The new OS features a number of optimizations designed to allow it to scale to the size and speeds necessary for the next-generation network and to do so in a more automated way that large service and cloud providers, such as Microsoft, AT&T, Comcast and Facebook, need.

The final piece of the Cisco puzzle was the release of new silicon photonics-based advances—also built into the new 8000-series routers—that allow the company to reach the 400G speeds per port that are necessary to power the future Internet. Leveraging several acquisitions that the company made in this area over the last several years, notably Lightwire and Luxtera, Cisco announced important advances in the field that are allowing them to reduce the manufacturing costs for these components by integrating them into more traditional silicon manufacturing processes. Given that the optics costs can reach 75% of the total when scaling to 400G and higher, that’s a critical step. Plus, as with the Silicon One family of chips, Cisco has decided to sell its silicon optics components separately for potential partners who may be interested.

While it’s easy to write off something called the “Future of the Internet” as little more than hype, Cisco managed to present a compelling case as to what problems lay ahead with current networking infrastructure equipment, the need for a new approach, and the achievements they made to address those needs. As with most behind-the-scenes technologies, we may not see the capabilities that Silicon One will bring to both our future wired and wireless connections, but if it’s all done right, we should most definitely experience it.

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Long-time semiconductor industry followers may recall that Arm, the chip IP company that completely dominates the smartphone market, has talked about making an impact on the enterprise and cloud computing markets for a very long time. Several years back, in fact, they made bold predictions about taking 20-25% of the server market. Despite a number of efforts in that direction from Arm semiconductor partners, however, that level of impact never occurred.

The company didn’t give up on its goals, though, and a few years ago, it unveiled a new brand, dubbed Neoverse, with a new chip architecture designed for infrastructure and other high-performance applications. However, those markets have been completely dominated by x86 processors from Intel and, more recently, AMD, so the initial acceptance of Arm-based compute engines—which often require recompiling or rewriting existing software—was modest.

Recently, the company has seen an enormous amount of momentum in the cloud computing space, capped by last week’s unveiling of the Amazon Web Services (AWS) Graviton2 CPU at Amazon’s re:Invent conference. Graviton 2 is a custom-designed SOC (system on chip), built on a 7nm process technology, based on sixty-four separate 64-bit Neoverse N1 cores that’s optimized for the kind of cloud computing applications for which AWS is known. As the name implies, this is actually the second-generation Arm-based chip from AWS—the original Graviton came out around this time last year. What’s particularly noteworthy about the Graviton2 is that it’s designed to directly compete on a performance basis with the high-end datacenter-focused CPU offerings from Intel and AMD. Best of all, the Graviton2 offerings come with a significant cost savings as well.

Traditionally, Arm’s promise in the datacenter and for large-scale cloud computing installations has been primarily about power savings—a critical factor when you’re talking about thousands and thousands of servers. With this new custom-designed CPU from AWS (leveraging the Annapurna Labs acquisition Amazon made back in 2015), however, the company is claiming to offer both power and performance improvements over existing solutions from Intel and AMD, as well as a reduction in cost. That’s a big step forward and, frankly, not something that many people expected could happen so soon.

The Graviton2 also reflects a level of commitment from Amazon that shows they are serious about increasing the diversity of the CPU suppliers and chip architectures that they want to support. In fact, the company launched the Graviton2 as part of its new sixth generation of what it calls EC2 (Elastic Compute Cloud) instances, which are intended for high-intensity workloads including application servers, micro-services, high-performance computing, gaming, and more. The original Graviton, on the other hand, supported a more limited set of general-purpose applications, such as web servers and data/log processing. In other words, Amazon is positioning its latest Arm-based offerings as serious competitors, on par with the big guys for some of the toughest workloads that exist. That’s about as strong an endorsement as you can get.

Part of the reason that Amazon is able to push Graviton2 so aggressively is that they’ve built a lightweight hypervisor layer, they call Nitro, that lets operating systems, applications, and utilities run independent of the underlying CPU architecture. As mentioned above, one the biggest challenges for Arm in the datacenter has been the need to either recompile or rewrite/refactor applications to work with the Arm instruction set, instead of X86, and that can often be a difficult, expensive process. Thanks to Nitro, however, Amazon is opening up a significantly wider array of software to be able to run on Graviton2-based devices. Because Amazon controls the whole hardware and software stack within AWS, they are able to create both hardware and software solutions that match their needs exactly, and that’s what they’re doing with Graviton2 and Nitro.

In fact, according to reports, Amazon plans to run a number of its own utilities and AWS services on Graviton2-based servers, including critical applications like load balancing, starting in 2020. More than just an interesting technical exercise, the reason AWS is doing this is because by leveraging their own hardware technology and software stack, along with the power and performance efficiencies enabled by the Arm architecture, the company can generate significant savings for its own operations thanks to the Graviton2.

Though no specific details were discussed, we may also see Arm-powered Graviton2 processors in edge computing applications, such as the new partnership that AWS also announced last week with Verizon to bring AWS to 5G networks in the US. The partnership will leverage Amazon’s new AWS Wavelength offering, which was specifically designed to take advantage of the ultra-low latency connections that are possible with 5G networks. AWS Wavelength will enable applications such as cloud-based gaming, autonomous industrial equipment, smart cities, connected AR and VR headsets, and much more to use AWS-powered compute resources at the very edge of the network. In the company’s press release, Amazon said that Wavelength will be used with EC2 instances. It seems logical and appropriate that Graviton2 might be used in those environments, because the power-based benefits of the Arm architecture always implied that it would be a good match for edge computing.

Several years ago, it would have been hard to predict that Arm-based chips could be part of such a significant announcement in the cloud computing world. AWS’ Graviton2 debut and the high-powered instances for which they are using it, however, clearly show that after a long build-up, Arm’s time to make an impact in the world of cloud and enterprise computing has finally come.

It’s hard enough keeping track and making sense of one technology megatrend at a time, but when you start trying to co-mingle two or even three of them together, well, generally speaking, all bets are off. Yet despite that seemingly unscalable challenge (and the buzzword bingo bonanza it implies), that’s exactly what the latest extension to a relatively new partnership between AT&T and Microsoft is attempting to do. In particular, the two companies are working to tie together cloud computing, 5G, and edge computing into a meaningful way. Even more surprisingly, this combination actually makes a great deal of sense and provides a tantalizing glimpse into the future of where all three of these major trends are heading.

Specifically, the two companies announced a new effort called Network Edge Compute (NEC) that would bring Microsoft’s Azure Stack cloud computing platform to network infrastructure equipment sitting at the edge of AT&T’s millimeter wave (mmWave)-based 5G network. The combination, which is currently available in the Dallas, TX region on a trial basis, will allow companies to start experimenting on new types of generation-defining applications that many believe are possible with the latest generation mobile network. It’s a chance to figure out what kinds of applications can be the Uber/Lyft, AirBnB, or Netflix of 5G.

At this point, no one really knows for sure what those new types of applications might be—just as no one could predict the rise of Uber/Lyft, AirBnB, or Netflix when 4G first came on the scene. However, there’s a general sense that something along those lines could (or will) happen, so it’s important to put the necessary infrastructure in place to make it happen.

Now, some might argue that this announcement isn’t really a big deal. After all, each of these elements have been available for a while and there has been discussion of some type of combination for some time. What’s particularly interesting, however, is that it’s the first time that these pieces have been connected in such a complete and real manner. Plus, having the combination of a telco carrier with a major cloud computing platform not only adds more overall “gravitas” to the offering, it also points out the practical reality that it’s likely going to take these kinds of new partnerships to drive applications and services forward in the 5G era.

From a technology perspective, the ability to leverage the lower latency connections possible with 5G in conjunction with the flexibility of container-based cloud-native applications running at the very edge of the network presents a new opportunity for developers. Because it’s new, it’s a computing model that make them a while to figure out how to best take advantage of.

Some of the efforts that the companies mentioned in their initial announcement provide a hint as to where these new capabilities may be headed. Cloud-based gaming, for example, is commonly touted as a great potential application for 5G because of the possibility of reduced lag time when playing games. Not surprisingly, AT&T and Microsoft talked about some early efforts in that area with a company called Game Cloud Network, which is working to figure out how to maximize the combination of speedy connectivity and rapid access to computing horsepower.

Another interesting application includes the possibility of leveraging Network Edge Compute to do faster and higher-resolution image rendering for AR headsets, such as Microsoft’s HoloLens. Microsoft has already demoed similar capabilities in a controlled environment, but to bring that into the field would require exactly the type of high-speed, quick access computing resources that this new combined offering enables.

Yet another area that has been discussed for potential 5G uses is IoT, or Internet of Things, because of the new network standard’s potential ability to handle links to billions of different connected devices. Along those lines, AT&T and Microsoft also discussed working with an Israeli startup called Vorpal, which creates solutions that can track drones in areas where they can cause problems, such as airports and other commercial zones. To track up to thousands of drones in real-time requires a great deal of sensor input and fast, real-time computing that can be done by the network instead of on the devices themselves. In fact, it provides a whole new level of meaning to former Sun CEO Scott McNealy’s famous quip that the network is the computer.

One of the interesting side benefits of this combined AT&T-Microsoft product offering is that it also starts to put some real meat on the bone of edge computing. Up until now, edge computing has been seen by many as a vague concept that meant a lot of different things to different people. With examples like the ones that the two companies are discussing, however, the idea of an intelligent edge becomes much more concrete.

In fact, all of a sudden, the ties between an intelligent cloud, a connected intelligent edge, and a compute-enabled intelligent network start to make a lot more sense, and the combination of the three starts to look a lot more prescient.

Now that we’re several years into the AI revolution, people are starting to expect that the applications they use will become more intelligent. After all, that was the high-level promise of artificial intelligence—smarter, more contextually aware applications that could handle tasks automatically or at least make them less tedious for us to do.

The problem is, that hasn’t really proven to be the case. Sure, we’ve seen a few reasonably intelligent features being added to certain applications. However, you’ve often had to go out of your way to find them, and interacting with them hasn’t often been intuitive.

Thankfully, we’re finally starting to see the kind of easy-to-use intelligence that many expected to see when AI-enhanced applications were first introduced. Some of the latest additions to Google’s G Suite productivity applications, for example, bring tangible enhancements to the common day-to-day tasks we all use.

A new beta version of Google Docs now has Smart Compose features—first introduced in Gmail last year—which can make automatic suggestions to your writing. For longer form documents created in Docs, Google’s AI-powered features have the ability to suggest entire sentences, not just individual words or phrases, and are likely to help speed up the writing process.

In addition, Docs also has neural network-powered technology to make better grammar and spelling suggestions within your documents. A small but very useful example is the ability to recognize words or acronyms that may be unique to an industry or even a company (such as an internal project code name) and automatically add those to the dictionary. Once that’s done, the feature can then recognize and correct when mistakes have been made in those new words.

For Google Calendar, the company is enabling the use of Google Assistant and voice commands to manage your calendar, including doing things such as creating meetings, updating the time and/or location, and more, all with spoken commands. It’s the kind of personal assistant technology that many people expected from the first generation of intelligent assistants, but didn’t get.

Similarly, the integration of Google Assistant into G Suite can now enable people to send quick email messages or dial into conference calls completely hands-free, thanks to voice commands and dictation. While these aren’t dramatic new features, they are the kind of simple yet practical things that AI-based intelligence is bringing to applications overall, and they’re indicative of what the technology can realistically do.

Finally, Google is integrating voice-based control of meeting hardware in conjunction with an Asus built Hangouts Meet Hardware device. Designed to integrate with a monitor and conference room cameras, the microphone and speaker-equipped box can respond to requests to start and end meetings, make phone calls, and more. In addition, Google added voice support for accessibility features to the device, such as being able to turn on spoken feedback for visually impaired users.

What’s interesting about many of these new G Suite additions is that they’re starting to leverage technological capabilities that Google first created in more standalone forms but are now incorporating into broader applications. Google Assistant capabilities, for example, are certainly interesting on their own and from a search-focused perspective, but they’re equally, yet differently, valuable as a true personal assistant feature for calendaring.

In fact, in general, it seems Google is starting to take advantage of a variety of core advances it has developed, particularly around areas like AI, analytics, and managing vast amounts of data, across many of its larger platforms, from G Suite to Google Cloud Platform (GCP) and beyond. Of course, this isn’t terribly surprising, but it’s certainly interesting to observe and highlights the potential that Google has to disrupt the markets in which it remains a smaller player.

In the world of enterprise computing, few topics are as hot as hybrid cloud and cloud-native containerized applications. Practically every company that sells to enterprise IT now seems to have an offering and/or an angle that speaks directly to at least one, if not both, of those areas.

Most of the attention, of course, comes from software companies or the software divisions of larger conglomerates because of the critical role that software plays in enabling these technologies. As a company that’s been known almost exclusively for hardware over the last several years, HP Enterprise (HPE) was seemingly at a significant disadvantage—at least until their announcement this week at the KubeCon conference.

In a move that was both surprising and encouraging, the company debuted a new Kubernetes-based tool called the HPE Container Platform that it says will help organizations hasten their adoption of hybrid cloud architectures by, among other things, allowing legacy, non-native applications to be containerized and managed in a consistent fashion. Ever since Dell Technologies’ purchase of VMWare, in particular, HPE has been seen by many as a company that understood and evangelized the concept of hybrid cloud but didn’t really have the tools to back up that vision. With its Container Platform, however, HPE now has what appears to be a solid set of software tools that will allow organizations to address some of their biggest challenges around legacy software modernization.

Unbeknownst to many, HPE has been acquiring a number of smaller software companies over the last few years, most notably BlueData and MapR. It’s the combination of those companies’ technologies, mixed in with a healthy dose of pure, open source Kubernetes, that gave HPE the software capabilities it apparently needed to build out this new hybrid cloud-friendly platform.

As HPE and many other companies have pointed out—and the market itself has started to recognize—cloud-based software technologies and public cloud-style computing-as-a-service capabilities are incredibly powerful, but they don’t work for all types of applications and all types of companies. In fact, IaaS (Infrastructure as a Service) and PaaS (Platform as a Service) services represent only a small percentage of the workloads in most companies. Because of costs, regulation, complexity, data gravity (that is, the attraction of applications and services to large amounts of data, much of which has yet to migrate to the cloud because of storage costs, etc.), and most importantly, the wealth of difficult-to-change legacy applications that still play an incredibly important role in organizations, there’s been a significant shift in thinking over the last 12-18 months or so. Instead of presuming that everything would eventually move to the public cloud, there’s been a recognition that a hybrid computing model that supports both public cloud and on-premise private cloud is going to be with us as the mainstream option for many years to come. In fact, there’s still a huge percentage of total computing workloads that don’t have much, if any, connection to the cloud at all.

On the one hand, that recognition has brought a new sense of vigor to the enterprise hardware computing companies like HPE, Dell Technologies, Lenovo, Cisco, etc. that many had essentially written off as dead a few years back when the general thinking seemed to be that everything was going to move to the public-cloud. On the other hand, there have been learnings from the consumption-based business models of cloud computing (e.g., witness HPE’s GreenLake announcements from earlier in the year and Dell Technologies On Demand offering from just last week), as well as the cloud-native software development model of containerized microservices. As HPE’s Phil Davis succinctly points out, “The cloud is not a destination — it’s an experience and operating model.”

The end result is that organizations want to figure out ways in which they can combine many of the benefits of that cloud-based operating model with the reality of their own on-premise hardware and legacy applications, while fulfilling the unique requirements of those older applications. HPE’s Container Platform—which is expected to be available in early 2020—attempts to merge the two worlds by containerizing older applications without having to go through the long, painful, and expensive process of rewriting or refactoring them.

More importantly, Container Platform provides the ability to run those containerized legacy applications (as well as regular cloud-native containerized applications) on bare metal servers, without having to incur the costs of running virtual machines—a clear knock at Dell Technologies, and more specifically VMWare. In addition, the HPE Container Platform’s other twist is that it can automatically provide access to persistent storage for these containerized legacy apps. Many older apps need persistent storage to run properly, but that’s not a capability that containers easily enable. As a result, this one requirement has prevented many apps from being modernized and moved to the cloud. By directly addressing this need, HPE believes it can work with its base of customers—who are more likely to be running legacy applications anyway—to move them to a unified environment based on containers. That, in turn, should let them more easily manage their applications in a consistent fashion, thereby saving costs and reducing complexity for IT organizations.

The logic and vision behind this new platform strategy are sound, and it’s encouraging to see HPE take a significant new jump back into the software world. It remains to be seen, however, how well the company can convince potential customers of its software acumen and its ability to function as a key software platform provider. For certain customers, the capabilities of the HPE Container Platform seem like they could be very appealing, but the world of enterprise software is extremely complex and fragmented. Others with large existing investments in other platforms might have a harder time making a switch. Still, this seems like a strong strategic move by HPE and its management team, and one that’s clearly going to point the company in some interesting and exciting new directions.

There have been some very interesting shifts and evolutions happening in the enterprise computing world over the last several years. It all started, of course, with the explosion of interest in cloud-based computing, as pioneered by Amazon’s Web Services (AWS) and then quickly followed by Microsoft’s Azure, IBM’s Cloud, Google Cloud Platform (GCP) and many more.

In the early days, there were untold proclamations and forecasts that virtually all business-focused-workloads would end up in the cloud, not just because of the nearly infinite range of computing resources the cloud provided, but because of the flexible pricing models allowing companies to only pay for what they used. This notion of consumption-based pricing was a radical concept at the time, particularly for an industry that had been based on paying a lot of money for expensive IT equipment, which sometimes sat unused or at other times proved to be woefully inadequate for a company’s real needs.

Fast forward to the present, however, and a much different picture has emerged. It turns out, trying to move everything into the cloud wasn’t practical and could get very expensive. As a result, it’s now widely recognized that most companies are trying to balance moving some of their workloads to the cloud, while keeping others on site within their own premises—a situation often shortened to “on prem.” For a variety of different reasons, including privacy, security, regulatory, monetary, computing architecture and more, the notion of “hybrid cloud” computing, in which you have a mix of off-site cloud computing workloads and some on-site private cloud workloads, has become the mainstream for enterprise computing.

Despite this pendulum swing back, however, it doesn’t mean that there wasn’t interest in some of the more radical types of usage, pricing, and consumption business models that cloud providers first introduced. The idea that companies didn’t have to own the physical computing assets that their workloads were using, in particular, was something that many companies latched onto. Essentially, they wanted to think about how they could move their IT investments from a capital expenditure to an operational expenditure, which allowed them to think about IT and what it provided as a service to the company in an entirely different way.

In fact, we’ve now seen a number of vendors pivot to start offering at least some of their enterprise-focused hardware on an as-a-service basis. HPE, for example, has said that within a few years they plan to offer everything they sell as a service (though, to be clear, they don’t expect everything to be purchased or consumed that way). At its annual analyst summit in Austin, Dell Technologies this week also took a big step in this direction with the announcement of a whole range of new “as a service” offerings called Dell Technologies On Demand that allows companies to have Dell-branded hardware installed within their datacenters, without an outright purchase. Instead, pricing is based on a consumption model in which companies pay for what they use.

Fundamentally, it’s a similar approach to what the cloud computing providers have offered, but now it’s being done for “on-prem” hardware. What’s even more interesting is that this is one of big announcements from the whole event and it is says a great deal about how the world of enterprise computing has evolved. For years, there were always dog-and-pony shows that highlighted the latest hardware (and software) advances, but now, instead of talking about features, companies like Dell Technologies are talking about business models and sales methodologies. And, importantly, it’s not only OK, it’s absolutely the right thing to do (and, arguably, the right time to do it).

Advancements in enterprise hardware and software are certainly going to continue. In fact, one of the other big announcements from this event was the new Dell EMC Power One system, which is a modular “datacenter in a box” that incorporates a hardware appliance that runs special microservices-based, cloud native, Kubernetes-managed software containers designed to automate a number of standard IT processes. The software features AI-powered intelligence that allows it to do tasks such as monitoring hardware, configuring VMware clusters, and dynamically assigning the required hardware demanded by certain workloads (in a cloud-like fashion), as needed. In essence, it brings the autonomous, dynamically shifting compute resources of the cloud into on-prem private cloud architectures.

What’s interesting though, is that we’re seeing increasing focus on very different aspects of the enterprise computing world that put less emphasis on speeds and feeds and much more on how companies can achieve the goals they have for IT organizations. Ultimately, it’s part of the long-term shift we’ve seen in organizations that expect to be able to use their IT capabilities in a digitally transformative way and that allows IT personnel to move beyond the humdrum maintenance of those resources into jobs that allow them to drive their organizations forward in more interesting and compelling ways.

In an era of smartphones with larger screens and more and more capabilities, some people have started to question the continued usefulness and viability of PCs. After all, the thinking goes, if I can do more on my smartphone, why do I need to carry a notebook around as well?

Theoretically, no company should know this better than Samsung. It’s pushing smartphone capabilities further than anyone with devices, like the Galaxy Fold, that bring true computer-like capabilities to your pocket.

And yet, rather than backing away from the PC market, the company is doubling down, having introduced several new lines of laptops since the beginning of the year, including the Galaxy Book S, a Qualcomm 8cx-powered device that runs Microsoft’s Windows 10 Home. With today’s launch of the Galaxy Book Flex and Galaxy Book Ion, two Intel 10th Gen Core-powered devices, as well as the announcement of the forthcoming Intel Lakefield-based version of the Galaxy Book S, Samsung is extending its mobile PC line even further.

Both the Galaxy Book Flex and Ion—each of which are available in 13.3” and 15.6” screen size versions—are part of Intel’s new Project Athena program. Launched with great fanfare at this year’s CES show, Project Athena is designed to reinvigorate the PC market, with the ultimate goal of creating more compelling, more focused computing experiences, ideally enabled through new types of technologies and even new form factors. In the near term, however, the more practical objectives are to provide better real-world battery life, better connectivity, slim designs, and more immediate responsiveness—in other words, to make PCs a bit more smartphone-like.

On top of those usability enhancements, another critical goal with Project Athena—and likely why Samsung views it as an important extension of its product line—is to offer the kind of robust performance that only a well-equipped PC can provide. The truth is, no matter how compelling smartphone performance becomes, there are certain tasks most people do that require the kind of performance and interaction that only a PC can deliver.

Whether it’s working with large sets of numbers, laying out large documents, editing videos or composing music, years of multi-device experience have shown us that PCs still play an important role—particularly for people who push their device capabilities to the limit and expect high-quality performance while working (or digitally playing) wherever they choose to. Throw in the desire/need to connect to a wide variety of powerful peripherals, and it’s clear that PCs have a healthy outlook, even in an era of powerful, foldable smartphones.

In that light, both the Galaxy Book Ion, which starts at under 1 Kg in weight, and the Galaxy Book Flex, which is based on a 2-in-1 design with a 360° hinge and integrated S-Pen, provide the kinds of key features and premium designs that are likely to appeal to these types of “mobile go-getters” (as Intel prefers to call them). Given Samsung’s heritage, it’s no surprise that the screen capabilities, in particular, look to be distinguishing characteristics. All four variants feature a full HD (1,920 x 1,080) resolution QLED panel that offers up to 600 nits of brightness, enabling an Outdoor mode for easy viewing outside. Both 15.6” models also offer the option of discrete Nvidia GeForce MX250 graphics with 2GB of GDDR5 memory. The Galaxy Book Ion 15 also features the ability to expand beyond its 16 GB DRAM and 1 TB SSD storage with an empty memory SoDIMM and a slot for additional SSD storage. All four are expected to be available in the US in 2020. Details on the Intel version of the now confusingly named Galaxy Book S are still to come.

Despite its growing PC ambitions, Samsung remains a niche player in the global PC market and these devices aren’t likely to dramatically change that. However, they are an important step forward for the company, and their very existence points to a bigger picture of multi-device and even ambient computing that Samsung seems to be embracing. In fact, given the growing relationship between Samsung and Microsoft, as well as the long-term existing partnership that Samsung shares with Google, the Korean giant is smartly moving itself into a unique and potentially very powerful position at the center of a diverse and growing universe of computing devices and platforms. Over time, Samsung could become the connecting thread that links together diverse computing worlds into a single unified experience and could prove to be an even stronger competitor to Apple.

Working with component providers like Intel and Qualcomm also plays right into that strategy and vision, because it provides them access to some of the key components they need to power that experience. Conversely, Samsung is a great partner for Intel to line up for Project Athena because of its capabilities in critical components (e.g., foldable displays) that could enable even more compelling computing devices.

Ultimately, all these companies need to work on making the experience of using multiple devices—which is now, and will continue to be, the day-to-day reality for the vast majority of consumers and business workers—much easier. Thanks to its uniquely broad product portfolio and range of platform and component partnerships, Samsung has the opportunity to make a big impact here. Let’s hope this is the start of more to come.

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.

As simple as it may sound, one of the hottest topics in the modern workplace is figuring out how to best collaborate with your co-workers. Given the preponderance of highly capable smartphones, the ubiquity of available webcams and other video cameras, and a host of software applications specifically designed to enhance our co-working efforts, you would think it would be a straightforward problem to solve. But, in fact, companies are expending a good amount of time, effort and money trying to figure out how to make it all work. It’s not that the individual products have specific issues but getting multiple pieces to work together consistently and easily in a large environment turns out to be harder and more complicated than it first appears.

Part of the challenge is that video is becoming a significantly larger part of overall inter- and intra-office communications. Thanks to several different factors including faster, more reliable networks, a growing population of younger, video-savvy workers, and enhanced emphasis on remote collaboration, the idea of merely talking to co-workers, customers and work colleagues is almost starting to sound old-fashioned. Yet, despite the growth in video usage, just under 5% of conference rooms are currently video enabled, presenting a large opportunity for companies looking to address those unmet needs. Plus, our dependence on smartphones has reached deep into the workplace, creating new demands for products that can let smartphone-based video and audio calls be more easily integrated into standard office workflows.

A number of companies are working to address these issues from both a hardware and software perspective, including Poly, the combined company formed by last year’s merger of Polycom and Plantronics, Zoom, the popular videoconferencing platform, and, of course, Microsoft, among many others. At this year’s Zoomtopia conference, Poly took the wraps off a new line of low-cost dedicated videoconferencing appliances, the Poly Studio X30 and Studio X50, both of which can natively run the Zoom client software, as well as other Open SIP-compliant platforms without the need for a connected PC.

The soundbar-shaped devices are built around a Qualcomm Snapdragon 835 SOC, run a specialized version of Google’s Android, and feature a 4K-capable video camera, an integrated microphone array, and built-in speakers. In conjunction with the Zoom application, they allow organizations to easily create a Zoom Room experience in a host of different physically different size spaces, from huddle rooms to full-size conference rooms. Plus, because they’re standalone, they can be more easily managed from an IT perspective, offer more consistent performance, and can avoid the challenges end users face if they don’t have the right versions of communication applications when connecting to USB-based video camera systems.

Leveraging the compute horsepower of the Qualcomm SOC, both devices also include several AI-driven software features called PolyMeeting AI, all of which are designed to improve the meeting experience. Optimizations for audio include the ability to filter out unwanted background noises, while new video features offer clever ways of providing professional TV production-quality video tweaks, doing things such as focusing on the current speaker, seeing overall meeting context and more.

Poly is also working with Microsoft’s Teams platform in another range of products called the Elara 60 series that essentially turn your smartphone into a deskphone. Most versions of the Elara include both an integrated speakerphone, a wireless Bluetooth headset, and an integrated Qi wireless charger that can be angled to provide an easy view of your smartphone’s display. By simply placing your smartphone on the device and pairing it via Bluetooth, you can get the equivalent of a desktop phone experience with the flexibility and mobility of a smartphone. Plus, thanks to the integration with Microsoft Teams, there’s a dedicated single Teams logoed button that lets you easily initiate or join a Teams-driven call or meeting—a nice option for companies standardizing on Teams as their unified communications platform.

Of course, the reality is that most organizations need to support multiple UC platforms because even if they make their own choice for internal communications, there’s no way to know or control what potential customers and partners may be using. Given the diversity and robustness of several different platforms choices—including Zoom and Teams, but also Blue Jeans, GoToMeeting, Webex, Ring Central and Skype among others—what most organizations want is a software-based solution that would allow them to easily switch to whatever platform was demanded for a given call or meeting. While that may seem somewhat obvious, the reality is that most videoconferencing products came from the AV industry, which was literally built on decades of proprietary platforms.

Thankfully, we’re reaching the point where it’s now possible to build collaboration and videoconferencing devices based on standard operating systems, such as Android, and then simply run native applications for each of the different communications platforms that are required. We’re not quite there yet, but it’s clear based on some of these new offerings that we are getting much closer.