Let’s talk about machine learning at the edge

ARM believes its architecture for object detection could find its way into everything from cameras to dive masks. Slide courtesy of ARM.

You can’t hop on an earnings call or pick up a connected product these days without hearing something about AI or machine learning. But as much hype as there is, we are also on the verge of a change in computing that’s as profound as the shift to mobile was a little over a decade ago. In the last few years, the results of that shift have started to emerge.

In 2015, I started writing about how graphics cores—like the ones Nvidia and AMD make —were changing the way companies were training neural networks for machine learning. A huge component of the improvements in computer vision, natural language processing, and real-time translation efforts have been due to the impressive parallel processing graphics processors have.

Even before that, however, I was asking the folks at Qualcomm, Intel, and ARM how they planned to handle the move toward machine learning, both in the cloud and at the edge. For Intel, this conversation felt especially relevant, since it had completely missed the transition to mobile computing and had also failed to develop a new GPU that could handle massively parallel workloads.

Some of these conversations were held in 2013 and 2014. That’s how long the chip vendors have been thinking about the computing needs for machine learning. Yet it took ARM until 2016 to purchase a company with expertise in computer vision, Apical, and only this week did it deliver on a brand-new architecture for machine learning at low power.

Intel bought its way into this space with the acquisition of Movidius and Nervana Systems in 2016. I still don’t know what Qualcomm is doing, but executives there have told me that its experience in mobile means it has an advantage in the internet of things. Separately, in a conference call dedicated to talking about the new Trillium architecture, an ARM executive said that part of the reason for the wait was a need to see which workloads people wanted to run on these machine learning chips.

The jobs that have emerged in this space appear to focus on computer vision, object recognition and detection, natural language processing, and hierarchical activation. Hierarchical activation is where a low-power chip might recognize that a condition is met and then wake a more powerful chip to provide necessary reaction to that condition.

But while the traditional chip vendors were waiting for the market to tell them what it wanted, the big consumer hardware vendors, including Google, Apple, Samsung—and even Amazon—were building their own chip design teams with an eye to machine learning. Google has focused primarily on the cloud with its Tensor Flow Processing Units, although it did develop a special chip for image processing for its Pixel mobile phones. Amazon is building a chip for its consumer hardware using tech from its acquisition of Annapurna Labs in 2015 and its purchase of Blink’s low-power video processing chips back in December.

Some of this technology is designed for smartphones, such as Google’s visual processing core. Even Apple’s chips are finding their way into new devices (the HomePod caries an Apple A8 chip, which first appeared in Apple’s iPhone 6). But others, like the Movidius silicon, use a design that’s made for connected devices like drones or cameras.

The next step in machine learning for the edge will be to build silicon that’s specific for the internet of things. These devices, like ARM’s, will focus on machine learning with incredibly reduced power consumption. Right now, the training of neural networks happens mostly in the cloud and requires massively parallel processing as well as super-fast I/O. Think of I/O as how quickly the chip can move data around between its memory and the processing cores.

But all of that is an expensive power proposition at the edge, which is why most edge machine learning jobs are just the execution of an already established model, or what is called inference. Even in inference, power consumption can be reduced with careful designs. Qualcomm makes an image sensor that that requires less than 2 milliwatts of power, and can run roughly three to five computer vision models for object detection.

But inference might also include some training, thanks to silicon and even better machine learning models. Movidius and ARM are both aiming to let some of their chips actually train at the edge. This could help devices in the home setting learn new wake words for voice control or, in an industrial setting, be used to build models for anomalous event detection.

All of which could have a tremendous impact on privacy and the speed of improvement in connected devices. If a machine can learn without sending data to the cloud, then that data could stay resident on the device itself, under user control. For Apple, this could be a game-changing improvement to its phones and its devices, such as the HomePod. For Amazon, it could lead to a host of new features that are hard-coded in the silicon itself.

For Amazon in particular, this could even raise a question about its future business opportunities. If Amazon produces a good machine learning chip for its Alexa-powered devices, would it share it with other hardware makers seeking to embrace its voice ecosystem, in effect turning Amazon into a chip provider? Apple and Google likely won’t share. And Samsung’s chip business is for its gear and others, so I’d expect its edge machine learning chips to find their way into the world of non-Samsung devices.

For the last decade, custom silicon has been a competitive differentiator for tech giants. What if, thanks to machine learning and the internet of things, it becomes a foothold for a developing ecosystem of smart devices?

Stacey on IoT | Internet of Things news and analysis

Qualcomm Technologies Announces the Introduction of Qualcomm Wireless Edge Services

Qualcomm Technologies Announces the Introduction of Qualcomm Wireless Edge Services

Qualcomm Technologies Announces the Introduction of Qualcomm Wireless Edge Services

Introduces Chipset as a Service model designed to facilitate mass scale and trusted device provisioning, security and lifecycle management.

Qualcomm Technologies, Inc., today announced Qualcomm® wireless edge services a set of trusted software services designed to meet the requirements of new Enterprise and Industrial IoT customers to securely provision, connect and manage long life-cycles of billions of intelligent wireless devices through their cloud platforms.

Qualcomm wireless edge services software is expected to be exposed through new APIs and available on select Qualcomm Technologies’ chipsets – initially the MDM9206, MDM9628 and QCA4020 – and later on, select Qualcomm Snapdragon™ platforms.

As the relevance, capability and intelligence of the wireless edge increases, Qualcomm Technologies continues to evolve its leading Snapdragon portfolio to better serve and accelerate the realization of the data driven transformation central to our 5G vision. Qualcomm wireless edge services is anticipated to facilitate the integration, processing, analysis, learning and trusted exchange of information with wireless edge devices and unlock new use cases, services, ecosystems and business models, creating additional value across many industries.

With security rooted in the chipset hardware, Qualcomm wireless edge services is designed to assist large enterprise, industrial cloud providers and users in provisioning and managing massive amounts of connected 4G and 5G devices in a trusted, security-rich and scalable manner. It is also engineered to support a new Chipsets as a Service (CaaS) business model, in which the value of certain chipset capabilities can be realized through services.

Specifically, Qualcomm wireless edge services is expected to provide trusted device services as well as strong protection against compromised devices and network attacks through hardware-based device integrity. Qualcomm wireless edge services is designed to facilitate the deployment of edge devices at scale, as well as efficient zero-touch life-cycle management through services such as plug n play onboarding, on-demand, over-the-air feature activation, emergency and routine upgrades as well as third party service enablement throughout the device life-cycle.

Serge Willenegger, senior vice president and general manager, 4G/5G and Industrial IOT, Qualcomm Wireless GmbH, said:

“With the introduction of Qualcomm wireless edge services, we continue to evolve and augment our leading Snapdragon portfolio to better serve our expanding base of customers across many industries and unlock the potential associated with trusted wireless access to billions of increasingly capable edge devices.”

“We are excited by the initial response from cloud, enterprise and industrial companies and are looking forward to working with them as well as our traditional customers to accelerate the transformative opportunity supported by advanced security, intelligence and wireless connectivity capability at the edge.”

Qualcomm wireless edge services is designed for and expected to be applicable across multiple segments and initially be offered on Qualcomm’s MDM9206 LTE Modem for Industrial IoT products, MDM9628 LTE Modem for Automotive products and QCA4020 for Home IoT products, which require extremely reliable connectivity and security. The initial support of Qualcomm wireless edge services on these chipsets is anticipated to be available in 2H 2018.

“At Alibaba, we are invested in developing leading-edge and reliable technologies to accelerate and support a broad range of commercial and industrial applications,” said Wei Ku, general manager, Alibaba Cloud IoT. “We see a strong synergy between Qualcomm wireless edge services and Alibaba Cloud Link, which will bring opportunities to expand security services and extend lifecycle management to customers, while also supporting greater feature management and flexibility.”

“We believe very close collaboration between cloud computing and edge devices is necessary to address the challenges of the industrial IoT ecosystem,” said Ray Guan, deputy general manager, Baidu Cloud. “Qualcomm wireless edge services is a very promising technology which will allow the ecosystem to unlock new use cases and business models, and lower the barrier of entry for many customers.”

“We are excited to collaborate with Qualcomm Technologies in this new initiative”, said Jack Huang, chief executive officer, Gizwits, China’s leading IoT development platform and cloud service provider, “as Qualcomm wireless edge services will complement and augment our IoT cloud offering, helping us innovate in both technology and business models.”

“At MeiG, we understand that device security is a core requirement for today’s growing IoT,” said Benjamin Du, chief executive officer, MeiG Smart. “We expect that Qualcomm wireless edge services will give us increased flexibility and built in support for developing products that can be easily integrated with cloud infrastructure.”

Said, Joe Xia, chief technology officer, Mobike:
“The subject of IoT Security is of critical importance to the field of shared mobility. Qualcomm wireless edge services enhanced security algorithm will help us to ensure the rights of our customers continue to be protected with the most advanced technology available. In addition, the flexible service structure offered by Qualcomm wireless edge services will help stimulate further innovation in IoT services across the industry.”

“Given the complexity of IoT requirements for modules and the very diverse verticals our products serve,” said Patrick Qian, chief executive officer, Quectel, “We anticipate that Qualcomm wireless edge services will be a fundamental tool to address the market needs with flexibility and scale.”

“As demand for device connectivity continues to grow across new use cases and industries, the requirements of the massive IoT are becoming increasingly more diversified,” said Wendy Wang, general manager, SIMCom Wireless. “SIMCom is providing service for an installed-base of 10,000 Enterprise customers with 100 million connections around the world today, we are excited about Qualcomm wireless edge services to help address such a big user base with strong hardware-based security design. With Qualcomm wireless edge services, we hope to continue to develop cost efficient LTE modules that meet the increasingly diversified connectivity, processing and security requirements without requiring an extra layer of hardware or software”.

Said Dr. Jun Zou, chief technology officer, Sunsea Group:
“At Sunsea we believe IoT opportunities can be fully addressed with the right integration of edge devices and cloud. Qualcomm wireless edge services is a fundamental step in this direction and we look forward to collaborating with Qualcomm Technologies to provide even richer services to our customer base.”

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IoT Business News

JV Utilises Deep Learning & Edge Computing To Make Factories Smart

Hitachi is aiming to utilize deep learning and edge computing technologies to make machines intelligent, in order to improve productivity. In pursuit of this, it has formed an automation joint venture with industrial robot and factory automation company Fanuc and AI-startup Preferred Networks.

Intelligent Edge System will utilize AI technologies in the social and industrial infrastructure field. It will develop fast, real-time control systems for network-connected industrial robots and machine tools. These control systems will leverage deep learning AI technology to become smarter over time as linked machines manufacture products.

Preferred Networks will use its deep learning AI technology to process information more efficiently and speed up data analysis. This is hoped to boost production line productivity and allow robots to recognize things and adjust their moves accordingly. Robots will also be able to automatically take on the task of an adjacent robot on the production line in case it breaks down.

Edge computing will help the initiative by handling the task at the edge of the network instead of centrally processing data. This will let machines on the production line process the massive amount of data, such as the movement of mechanical hands, on the spot.

Preferred Networks has already applied its AI expertise for Toyota Motor and Nippon Telegraph & Telephone. Toyota Motor invested in the startup for the development of autonomous vehicles that can learn various driving conditions by processing data by themselves rather than relying on cloud computing.

http://www.hitachi.com/New/cnews/month/2018/01/180131f.pdf

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Verizon Transforms Intelligent Edge Network without Impacting Customer Traffic

Verizon has come up with a solution to improve customer experience and increase efficiency on its Intelligent Edge Network. The company has been driving customer traffic on the transport network circuit emulation solution from Cisco. In the past, circuit emulation equipment carried speeds up to OC-12, while Cisco’s new platform supports speeds up to OC-192.

The process of circuit emulation involves transport of conventional digital and optical signal rates over a packet-based MPLS network. This does not affect customer traffic and facilitates transmission of legacy services to next-generation infrastructure. Verizon has been working with Cisco to develop, test and implement this solution. The partnership has planned to increase the number of circuits in the coming years. Cisco feels this will promote growth of Ethernet services and improve the reliability of mission critical TDM private line services.

In the 100G U.S. metro network rollout, Verizon had used this technology to aggregate multiple Ethernet and TDM circuits within the same location. Moreover, features such as video streaming, social media, and cloud services have affected the network traffic. It will also improve the Verizon’s cost structure and customer’s digital experience.

Lee Hicks, vice president for network infrastructure planning for Verizon, said, “In the face of robust customer demand on our network, Verizon’s infrastructure systems must adapt to support current and future needs. By implementing innovative deployments like this high-capacity circuit emulation, Verizon continues to set the bar for the highest standards of network performance and efficiency.”

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