More people than ever are relying on telecoms networks to work, play, and stay connected during the pandemic. Operators are doing all they can to ensure their existing networks have enough capacity to cope with demand.

Gorkem Yigit, Principal Analyst at Analysys Mason, said:

“Video streaming continues to experience high year on year growth and that has been exacerbated by the pandemic and resulting lock-downs,

Yes, 5G grabs the spotlight, but 4G is carrying the brunt of this traffic. So, while investment in 5G infrastructure continues, operators need intelligent ways to maximize and extend existing 4G network capabilities in the short to medium term – keeping their CAPEX to a minimum.”

8 out of 10 of the world’s largest operator groups have deployed traffic management technology from the Openwave subsidiary of Swedish firm Enea. Many of these have since upgraded to include machine learning capabilities.

Openwave claims that, based on its figures, some operators faced a 90 percent surge in peak throughput during lockdowns.

Machine learning is helping to predict and identify congestion in the RAN (Radio Access Network) which resides between user equipment such as wireless devices and an operator’s core network.

John Giere, President of Enea Openwave, commented:

“Conventional mobile data management requires manual configuration and network investment – it is no longer fit for purpose.

Machine Learning has given existing 4G networks the shot in the arm they needed. It can work dynamically without external probes or changes to the RAN, delivering additional capacity at a time that operators most need it.” 

The use of machine learning has increased operators’ 4G RAN capacity by 15 percent in congested locations—providing further evidence of how AI technology can be used to quickly tackle real-world problems.

(Photo by Adrian Schwarz on Unsplash)

The post AI is helping mobile operators to cope with pandemic demand appeared first on AI News.

There are two new chips in the 7-series – the Snapdragon 730 and 730G – along with one in the 6-series, the Snapdragon 665.

Kedar Kondap, VP of Product Management at Qualcomm, says:

“With the introduction of the Snapdragon 730, 730G and 665 Mobile Platforms, we are bringing features such as sophisticated AI, exceptional gaming and advanced camera capabilities to a broad spectrum of devices at exceptional performance.

Each iteration of Snapdragon drives immense innovation that will surpass customer expectations.”

The base Snapdragon 730 boasts double the AI processing power of its predecessor using Qualcomm’s 4th-gen multi-core AI Engine. A Qualcomm Hexagon 688 processor inside the 730 supports improved base scalar and Hexagon Vector eXtensions (HVX) performance, as well as the new Hexagon Tensor Accelerator.

Qualcomm has also packed in the Spectra 350 which contains a dedicated ISP for computer vision. It claims to provide up to four times as much power savings as its predecessor.

As for the Snapdragon 730G, that ‘G’ stands for ‘gamer’ and intends to boost the gaming performance of devices packing it.

While the 730 features the Adreno 618 GPU, the first time a Qualcomm 7-series chip has supported the Vulkan 1.1 graphics API, the 730G claims to offer 15 percent faster rendering. Furthermore, Qualcomm claims it’s “collaborated with consumers’ favorite game-makers to optimize Snapdragon 730G for a selection of the world’s top-rated games.”

A dedicated ‘Jank Reducer’ feature boasts that it can reduce stutter by up to 90 percent in games running at 30FPS.

Perhaps most impressive, however, is the Snapdragon 665 which brings more capable AI processing abilities to lower-end devices.

While the new 7-series chips feature the 4th-gen, the Snapdragon 665 is packing the 3rd-gen of Qualcomm’s AI Engine with the Hexagon 686 DSP. It too claims to offer double the performance of its predecessor.

Packing the Spectra 165 ISP, the Snapdragon 665 provides AI-powered scene recognition and auto adjustments like HDR. To account for the growing number of unique camera setups on today’s smartphones, it supports up to three cameras which can include telephoto, wide-angle, and ultra-wide lenses.

Qualcomm has also partnered with Morpho to enhance the camera functionality of the Snapdragon chips.

Toshi Torihara, VP of Morpho, commented:

“Morpho is proud to collaborate with Qualcomm to integrate our image processing software on mobile. With the Snapdragon 665 mobile platform, we brought MovieSolid, our state-of-the-art image stabilisation implementation, to deliver industry-leading video experiences to the mobile camera industry.

We believe this partnership will further enhance smartphone camera capabilities, bringing mobile imaging & AI technologies to the next level.”

For gamers, the Snapdragon 665 features the Adreno 610 GPU which also supports the Vulkan 1.1 graphics API to help provide an improved gaming experience for those who on lower-end devices.

Commercial devices based on the Snapdragon 730, 730G, and 665 are expected to be available in mid-2019.

deepgeniusai.com/">AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, , & .

The post Qualcomm boosts Snapdragon AI performance with new chips for high and mid-tier devices appeared first on AI News.

AI-based tasks on most smartphones today are often carried out in the cloud without the average user even being aware. Google Photos, for example, uses deep learning to artistically crop and edit landscape panorama photos like a human photographer. Photos are categorised automatically, and — as of an update earlier this year — the app will even prompt who you may wish to share the pictures with.

Dedicated AI chips will allow faster processing of various tasks on the hardware itself without a need for the cloud.

Current Mobile AI Chips

While still in their infancy, we’re starting to see dedicated mobile AI in handsets before we even head into next year.

Huawei is leading the pack with its Kirin 970 chip which features a dedicated NPU (Neural Processing Unit). The company is claiming its NPU is able to perform an image recognition test on 2,000 images per second, 20 times faster than what the competition can do with a CPU alone.

It’s well known in the mobile industry that new technologies often don’t reach mainstream adoption until Apple gets on board. Fortunately, Apple isn’t far behind and will have dedicated AI cores in the iPhone X’s new A11 Bionic chip to power a range of new features including facial recognition and real-time analysis of 4K video for improving video recordings.

Huawei’s marketing team have been poking some fun at Apple’s expense. After the onstage failure of FaceID at the iPhone X’s announcement, Huawei posted an image of a clown with the caption “Let’s face it, facial recognition isn’t for everyone” alongside the hashtag #TheRealAIPhone ahead of the expected announcement of the Mate 10 Pro on October 16th.

We look forward to seeing whether Huawei can prove itself to be better at mobile AI than Apple, or whether Huawei will ultimately end up looking the clown.

Future Mobile AI Chips

Another given in the mobile industry is whatever Apple has, Samsung will either have or won’t be far behind.

Industry sources familiar with Samsung’s chip plans report the company has been carrying out R&D work into mobile AI chips.

“[Samsung] is in the middle of developing several types of chips that will be capable of processing massive data from AI applications on devices, eliminating the need to communicate with cloud servers,” a source from one of Samsung’s partners told The Korea Herald.

Samsung recently overtook Intel as the leading chip maker which makes this news particularly notable. We’re sure to see a dedicated AI chip debut in an upcoming Galaxy smartphone but we can also expect them in the company’s popular TVs and smart home technology.

“In the next three years or so, smartphones will have designated chips that will improve the processing of AI features by 50 percent from today,” the industry source explained. “A kind of an accelerator can be applied to empower AI devices.”

Investor Excitement

The potential for AI has excited investors who are putting their cash into chip manufacturers set to gain from this new venture.

Nvidia, who relies on the gaming market, is moving into AI and now provides chipsets for some of the world’s largest companies to power their efforts. The gaming market is often cyclical in nature but AI looks set to provide a healthy stream of revenue over the coming years.

The company’s main source of revenue is still the gaming market with $1.03 billion in sales in the first quarter of its fiscal year — although this was down from its average forecasts of $1.13 billion. However, Nvidia reported more than $400 million in quarterly revenue for its data center business selling chipsets to the many companies increasing their computing power for servers with deep learning technology.

These financial results arrive in tandem with a better-than-expected performance from Nvidia’s segment focusing on AI for self-driving cars and other automotive efforts. Combined, this more than made up for the shortfall in gaming revenue and showed investors how AI will provide a huge boon to chipmakers like Nvidia.

As a result, of any company in S&P 500 Index, Nvidia had the largest stock increase in 2016 and its shares have gained 227 percent over the past year.

“AI is the most important technology development of our time, with the greatest potential to help society,” says Nvidia Corp. Chief Executive Jensen Huang. “As the world’s leading cloud providers deploy the world’s best AI platform, with Volta GPUs and Nvidia software, we’ll see amazing breakthroughs in medicine, autonomous transportation, precision manufacturing and much more.”

Companies including Alibaba, Baidu, and Tencent are upgrading their data centers with Nvidia’s Volta-based platforms. It’s clear to us, the invasion of the dedicated mobile AI chips is nigh.

Are you excited about dedicated mobile AI chips? Share your thoughts in the comments.

The post Editorial: The invasion of mobile AI chips is nigh appeared first on AI News.