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Intel Capital commits $132 million to 11 AI startups

May 12, 2020 Big Data

Intel today announced that Intel Capital, its global investment organization, committed a total of $ 132 million to 11 startups focused on AI, automation, and chipset design. It follows a year in which the firm invested $ 466 million in 36 new companies (and 35 follow-on investments) and led 72% of its deals through 22 successful exits. In 2020, Intel Capital says it’s on track to invest around the same amount — between $ 300 million and $ 500 million — in startups specializing in AI, with a particular focus on intelligent edge devices and network transformation.

Intel doubling down on AI and machine learning is business as usual. During an earnings call late last year, CEO Bob Swan said the company generated $ 3.8 billion in AI-based revenue in 2019, and that he anticipates the market opportunity will reach $ 25 billion by 2024. To position itself for growth, Intel recently acquired Habana Labs, an Israel-based developer of programmable AI and machine learning accelerators for cloud datacenters, as well as Moovit, a mobility startup that could be central to Intel subsidiary Mobileye’s plans for a robo-taxi service.

Intel Capital’s expanded investment portfolio includes Redwood City, California-based Anodot, which uses machine learning to perform autonomous business monitoring for clients across telco, finance, and digital sectors. The startup’s platform for real-time, contextual alerts helps to spot incidents — e.g., drops in success rate, customer incidents, app performance, and business metrics — that might impact revenue and costs. In fact, Anodot says it cuts incident management by as much as 80%.

Astera Labs, another new Intel Capital investment, is a fabless semiconductor company headquartered in Santa Clara, California that develops purpose-built connectivity solutions for data-centric systems. It aims to remove performance bottlenecks in compute-intensive workloads like AI and machine learning, with products that span system-aware semiconductor integrated circuits, boards and services, and more.

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Intel Capital’s investment in Hypersonix could bolster the San Jose, California-based company’s autonomous analytics platform, which targets consumer industries like retail, restaurants, hospitality, and ecommerce. Through voice and text search and data visualization, Hypersonix provides real-time actionable insights from disparate data sources like regional business performance and website traffic.

Zhejiang, China-based KFBIO, which also received an Intel Capital investment, builds pathology systems such as a scanner that ostensibly improves on traditional microscopes with digital capabilities and connectivity. Using a combination of big data, cloud computing, and AI, the company’s medical image processing tech scans and digitizes images to make them easier to share for remote consultations with experts.

New Intel Capital portfolio company Lilt provides AI-powered language translation software and services. The San Francisco, California-based firm taps machine learning, a translation management system, and professional translators to enable organizations to scale their localization programs and improve their customers’ experiences.

As for Retrace, which recently received a cash infusion from Intel Capital, it applies machine learning to real-time data to improve dental decision-making. The San Francisco startup’s predictive platform aims to reduce the oral disease burden for health plans, providers, and patients by creating a more cost-effective and evidence-based oral healthcare experience.

Other new Intel Capital investments in 2020 include:

Axonne, which is developing next-generation high-speed Ethernet network connectivity solutions for automobiles.
MemVerge, which provides petabyte-size pools of shared persistent memory and data services for AI, machine learning, and high-performance computing workloads.
ProPlus Electronics, an electronic design automation company specializing in device modeling and fast circuit simulation solutions.
Spectrum Materials, a high-purity gas and material supplier for semiconductor fabricators.
Xsight Labs, a developer of chipset designs that promise to enhance scalability, performance, and efficiency.

Intel Capital, which launched in 1991, made a total of 1,588 investments prior to today’s announcement, according to Crunchbase data. Its new commitments soberingly come as an estimated 390 startups, including companies like Uber and Airbnb, have laid off over 44,600 employees as a result of pandemic-related economic headwinds. Crunchbase anticipates a “more pronounced” reset in investments as venture firms reassess their existing funding needs.

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Big Data – VentureBeat

Intel acquires urban mobility startup Moovit for $900 million

May 4, 2020 Big Data

Intel has confirmed that it’s buying Israeli urban mobility startup Moovit, in a deal worth $ 900 million. Reports of the impending acquisition were first published by local Israeli publication Calcalist on Sunday, and in a press release today, Intel noted that it was buying Ness Ziona-based Moovit to help make Mobileye a “complete mobility provider,” which will eventually include driverless taxi services.

Chip giant Intel has a recent track record of acquiring Israeli startups. Just a few months back it paid around $ 2 billion for Habana Labs, which develops programmable AI and machine learning accelerators for datacenters. But back in 2017, Intel doled out an eye-popping $ 15.3 billion for Mobileye, a computer vision firm specializing in autonomous cars. Mobileye’s advanced driver-assistance systems (ADAS) are currently used in 60 million vehicles, and as the technology moves further toward full autonomy, Moovit’s arsenal of data will help Mobileye turn on “cost- and demand-optimized driverless” car services.

When the deal closes, Moovit will become part of the Mobileye business but will continue to operate under its own brand and run its existing partnerships. It’s also worth noting that Intel had previously invested in Moovit via its venture capital arm Intel Capital, meaning that the value of the acquisition is actually pegged at $ 840 million, net of Intel Capital equity gain.

The story so far

Founded in 2012, Moovit is best known for its consumer-facing app that’s used by more than 40 million active users globally to see the best ways to traverse a city using a combination of transport options, and even includes nifty augmented reality (AR) directions. However, the company has pivoted its core business to focus on licensing its back-end platform to third parties through a “mobility-as-a-service” (MaaS) offering. Through this, Moovit provides municipalities with data and analytics to improve city transport infrastructure, while corporations such as TomTom and Microsoft also leverage Moovit’s data to offer third-party developer access to real-time transport data to include in their own apps.

Intel acquires urban mobility startup Moovit for $900 million

Above: NYC: Moovit’s AR-powered “way finder” feature

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The value here for a company like Intel and its Mobileye subsidiary is data, which Moovit aggregates from myriad transit partners and corporations, as well as from its 40 million active users who generate more than 6 billion data points each day around traffic flow and user demand. Mobileye has made no secret of its plans for so-called “robotaxis,” and Moovit’s data will prove vital as it pushes into the driverless MaaS sphere.

Indeed, Intel and Mobileye intend to sell and lease self-driving vehicles for other companies and organizations to operate, while they also plan to deploy their own robotaxi services. But having the technology to operate fleets of driverless vehicles at scale is only part of the picture, and data and “customer-facing infrastructure” will also be required, according to Mobileye CEO Amnon Shashua.

In other words, Moovit gives Mobileye the knowledge it needs as it gears up to commercialize autonomous vehicle services in key markets around the world. Through the Moovit mobile app, Mobileye can push its robotaxi services as part of the broader trip planner offering, meaning that a commuter may see that the best way to get to their office is to take a driverless taxi from their home to the station two miles away, and then get a train the rest of the way.

The timing of Intel’s acquisition is notable, as it comes during a period of great uncertainty for companies such as Moovit — the global COVID-19 crisis has meant that public transport has been in low demand as people have stayed at home. Moovit itself publishes data regarding the impact of COVID-19 on public transit usage in key cities around the world, showing that it’s down by as much as 80% in some areas.

Above: Moovit data showing decline in public transit during COVID-19

As with other companies during the COVID-19 crisis, Moovit has sought new ways to keep business going. Last month it launched an emergency mobilization platform that makes it easier for transit organizations to redeploy their unused vehicle fleets to create new on-demand transport services for frontline workers. Corporations can also use the platform to arrange dedicated pickup services to get their essential employees safely to their place of work.

It’s not clear what role — if any — the COVID-19 crisis played in Moovit’s decision to sell now, but under the wings of a tech behemoth such as Intel, Moovit can worry just that little bit less about revenues in the coming months and years as society adjusts to what could become a “new normal” that leans heavily on remote working. The terms of the deal also seem like a decent exit for Moovit, which had raised around $ 131 million since its inception, including the $ 50 million series D cash injection led by Intel Capital in 2018.

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Intel, Mozilla, and others join pledge to make IP freely available to fight coronavirus

April 8, 2020 Big Data

A consortium of organizations including Intel, Mozilla, and Creative Commons have joined the Open COVID Pledge, an effort led by legal experts and scientists to make intellectual property (IP) available for the fight against COVID-19. The aim is to bolster cooperation in pursuit of an end to the coronavirus pandemic; companies, institutions, and universities will give free licenses to their patents, copyrights, and certain other property rights to anyone developing technologies for the diagnosis, prevention, or treatment of COVID-19.

The licenses in question are effective December 1, 2019, and they’ll last until a year after the World Health Organization declares the coronavirus pandemic to be over. Companies who make the pledge must adopt the Open COVID license, create a custom license that accomplishes the intent of the pledge, or identify existing licenses that accomplish the pledge’s goal.

According to Mark Lemley, the director of the Stanford University program in law, science, and technology, the COVID Pledge is intended to prevent researchers and entrepreneurs from being sued for tools they create during the pandemic. Once things return to normal, the hope is that companies will work together to come up with commercially reasonable license terms, but they’re able to return to owning and asserting their intellectual property if they choose.

“While we have written a model license anyone can use, many universities and companies have their own license language and terms, and that’s fine. We are encouraging them to commit to the pledge, and they can do that while implementing the pledge with their own license terms,” said Lemley in a statement.

To date, the Open COVID Pledge has received expressions of support from organizations including DLA Piper, Unified Patents, the Idea Laboratory for Intellectual Property, Fabricatorz Foundation, Universities Allied for Essential Medicines, the University of Utah S. J. Quinney College of Law, and the Program on Information Justice and Intellectual Property at American University Washington College of Law. Creative Commons says it will continue to work with these and other experts to create a framework that allows the development of diagnostic tools, treatment, and preventative solutions — and possibly even a cure or vaccine — to halt the spread of COVID-19.

“We are … giving COVID-19 scientists and researchers free access to Intel’s vast worldwide intellectual property portfolio — one of the world’s largest — in the hope and belief that making this intellectual property freely available to them will save lives,” said Intel executive vice president and general counsel Steven Rodgers in a blog post. “We will continue to invent — and protect — our intellectual property, but we offer it freely to those working to protect people from this pandemic.

Intel separately announced this morning that it would donate $ 50 million in cash and resources to anti-coronavirus efforts.

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Intel and partners are using computer vision to help save Antarctica’s penguins from extinction

January 20, 2020 Big Data

Antarctica’s emperor penguin population has suffered such severe climate-related breeding issues that it’s at risk of disappearing by the year 2100, according to a 2019 study by the British Antarctic Survey. In search of a solution, an Intel-led group of tech companies developed a computer vision solution to help ecologists count the remaining penguins faster and more accurately than before.

The coalition consists of Intel’s AI Builders — the company’s curated ecosystem of software vendors — along with Microsoft’s AI for Earth initiative and data science consultancy Gramener. The consultancy sourced a corpus containing photos of Antarctica’s penguin colonies from Oxford University’s Penguin Watch Project, which over the past decade has sourced millions of time-lapse images from camera traps in over 40 locations and recruited online volunteers to annotate them. It then fed the data through a convolutional neural network — a type of AI model most commonly applied to analyzing visual imagery — that preserved spatial information while localizing penguin counts and estimating overall tallies.

The AI model was cascaded and two-stage in design. The first classified images into broad image categories, while the second estimated the density by taking inputs from earlier stages to generate maps. By approximating the number of penguins in clusters of different sizes, this density-based approach managed to handle most of the imaging challenges.

Intel Xeon Scalable processors were used to train the model within a virtual environment on Microsoft’s Azure cloud platform, which was then benchmarked using Intel’s Optimization for PyTorch toolkit. From this benchmarking exercise and other open source tools and libraries, Gramener says it was able to reduce training time from days to hours.

It was quite the feat, all told. Some images exhibited perspective distortion, where penguin faces in the foreground appeared larger than those in the background, and occlusion, where penguins in the background were hidden from view by objects or other penguins in the foreground. There was variability with respect to image quality and shooting angles, owing to unpredictable weather conditions, and there were often differences within the same picture, with some portions containing unusually high clusters of penguins.

Intel and partners are using computer vision to help save Antarctica’s penguins from extinction

Above: The AI system picks out penguins in a crowd.

Image Credit: Intel

“We faced multiple challenges. Almost 70% of images were unusable, as either the lighting wasn’t good or it was foggy, and penguins weren’t visible,” said lead data scientist Soumya Ranjan Mohanty. “These are easy for the human eye to perceive but difficult for an algorithm.”

It isn’t Hyderabad-based Gamener’s first AI project of an ecological nature. The consultancy partnered with Microsoft to build a model capable of classifying thousands of different flora and fauna species, using open source data from iNaturalist. It also developed a two-step solution that weeds out unusable images from camera traps and identifies those that contain animals, which became Microsoft’s Camera Trap API, as well as a system that provides high-resolution land cover information. Separately, Gamener worked with the Nisqually River Foundation to pick out fish species by analyzing video footage from an underwater camera that’s activated when fish pass certain infrared sensors. And in Kenya it worked with local researchers to build a model that could distinguish elephants from other livestock in aerial photographs.

Of course, Gamener is far from the first to apply AI to these kinds of problems. In December 2019, Google partnered with Conservation International and other organizations to process one of the world’s largest and most diverse databases of photographs taken from motion-activated cameras. DeepMind last year detailed ecological research its science team is conducting to develop AI systems that will help study the behavior of animal species in Tanzania’s Serengeti National Park. Microsoft recently highlighted a Santa Cruz, California-based startup called Conservation Metrics that’s leveraging machine learning to track African elephants, akin to a separate effort by an independent team of researchers to develop an algorithm trained on Snapshot Serengeti that can identify, describe, and count wildlife with 96.6% accuracy. And Intel’s own TrailGuard AI system prevents poaching by detecting motion with cameras using an on-device AI algorithm.

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Intel researchers propose AI that recognizes faces from thermal images

January 10, 2020 Big Data

Is thermal imagery detailed enough to enable an AI model to recognize peoples’ facial features? That’s the question Intel and Gdansk University of Technology researchers sought to answer in study recently presented at the Institute of Electrical and Electronics Engineers’ 12th International Conference on Human System Interaction. In it, they investigated the performance of a model trained on visible light data that was subsequently retrained on thermal images.

As the researchers point in a paper describing their work, thermal imagery is often used in lieu of RGB camera data within environments where privacy is preferred or otherwise mandated, like medical facilities. That’s because it’s able to obscure personally identifying details like eye color and jaw line.

The team tapped two data sets of facial thermal images., the first of which — SC3000-DB — was created using a Flir ThermaCam SC3000 infrared camera. It contained 766 images of 40 volunteers from a cohort of 19 men and 21 women, who were asked to sit and look at the camera for a span of two minutes. As for the second data set — the IRIS data set, from the Visual Computing and Image Processing Lab at Oklahoma State University — it contained 4,190 images collected with the assistance of 30 people, many of whom moved their heads and used different facial expressions.

The researchers first cropped the images to regions containing only peoples’ faces, using a machine learning model. Then, they employed another model to extract facial features from the images and represent them numerically, as vectors (sets of values corresponding to specific features). Lastly, they employed a third model trained on visible light images to validate whether such a model could be applied to thermal images.

In the course of experiments, the researchers compared two types of facial feature vectors: one that identified the person depicted in a given image and a second based on the similarity of the person’s profile with an input image. They found that the model trained on visible image data generalized well to thermal images, successfully extracting facial features and distinguishing among volunteers with accuracies of 99.5% for SC3000-DB and 82.14% for IRIS.

The team hopes to extend their research to data collected in various other scenarios, such as when subjects’ heads are turned horizontally or vertically. “Many promising visual-processing applications, such as non-contact vital signs estimation and smart home monitoring, can involve private and or sensitive data, such as biometric information about a person’s health,” wrote the researchers. “Thermal imaging, which can provide useful data while also concealing individual identities, is therefore used for many applications.”

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Former Apple chipmakers form Nuvia to rival Intel and AMD server CPUs

November 17, 2019 Big Data

(Reuters) — Three of Apple’s former top semiconductor executives in charge of iPhone chips have founded a startup to design processors for datacenters, aiming to take on current industry leaders Intel and Advanced Micro Devices.

Nuvia was founded by Gerard Williams III, Manu Gulati, and John Bruno in early 2019 and is developing a processor code-named Phoenix. The company on Friday said it raised $ 53 million from Dell Technologies Capital and several Silicon Valley firms, which will help it expand from 60 employees to about 100 by the end of this year.

The company’s founders, backers, and plans have not been previously reported.

Williams left Apple this spring after more than nine years as chief architect for all Apple central processors and systems-on-a-chip. Gulati spent eight years at Apple working on mobile systems-on-a-chip, and Bruno spent five years in Apple’s platform architecture group. Gulati and Burno also worked for Alphabet’s Google before coming to Nuvia.

At Nuvia, the group’s goal is to take the lessons learned designing powerful chips for small, battery-powered devices such as iPhones and apply them to large, electricity-hungry datacenter servers. The founders aim to make a chip that is faster, more power-efficient, and more secure than existing datacenter processors, Williams told Reuters.

“We want to bring all these aspects that we have developed over time through our careers to this new market and really exploit them in this market, because it’s an area ripe for innovation and advancement,” Williams told Reuters in an interview.

Chip suppliers Qualcomm, Marvell Technology Group and Ampere Computing, a startup founded by Intel’s former president, are all also trying to take on Intel and AMD in the server market with chip technology previously used in mobile phones.

But Patrick Moorhead, an analyst at Moor Insights & Strategies, said Nuvia could prove a formidable new entrant based on its founding team’s track record. He said the team made “unprecedented” performance gains with each new generation of chips at Apple.

“I’ve been in and around semiconductor companies for 30 years. There are never any guarantees, but one thread that I’ve seen is that the most successful chip companies have rock star architects and developers,” Moorhead said.

Among Nuvia’s investors is Dell Technologies’ venture arm. Dell is one of Intel’s biggest customers. Scott Darling, president of the venture group and himself a veteran of Apple and Intel, said he could not comment on whether Dell will use Nuvia’s chips.

“The silicon industry is relentless. It pounds forward,” he told Reuters. “You have to have a world-class team to do something disruptive enough that it will prevail despite the response of competitors. At Nuvia, we think we have found one.”

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Why Intel is well-positioned against AMD and Nvidia

May 27, 2019 Big Data

Semiconductors are in a bit of a gully right now. IDC predicted last week that after three consecutive years of growth, the market will decline 7%. Ironically, chip shortages have in part led to slower growth in certain markets. Unsurprisingly, NAND prices have plummeted, and there’s even mention that Intel will see declines in its datacenter business. Earnings in the category have also been mixed, leading equities analysts to downgrade certain companies, if not the entire category. Those who have been more optimistic about the category seemed to pin their hopes on a new trade deal with China. With such hopes fading quickly, it certainly puts a dark cloud over the entire industry.

So, in the short term, some investors may take pause. It’s important to remember that the need for compute, and therefore compute semiconductors, isn’t about to slow down — not with the world’s technology community focused on growth areas like artificial intelligence and analytics, high-performance computing, gaming, autonomous vehicles, delivery drones, and 5G. Most of these technologies are powered by the bigger compute and graphics processing capabilities of Intel, Nvidia, and AMD. These companies will battle hard for market share, and while it’s been a popular sentiment to discount Intel, I believe there are fundamental reasons that in the end, Intel has the best chance of coming out on top.

A stronger portfolio and ecosystem

Take a look at where each of these companies is focused. AMD’s categories of focus include PC, gaming, and datacenter. Nvidia also focuses on PC, gaming, and datacenter, as well as automotive. Of the trio, Intel is by far the most diversified, competing in five categories: compute, storage, autonomous vehicles, network/carrier, and IoT.

Intel has also significantly extended its definition of compute to include several new areas, like integrating machine learning into its server compute chips. The company also plans to launch its own discrete consumer-oriented graphics cards by 2020 and discrete Nervana machine learning accelerators yet this year (although conservative estimates might add a year to either plan). The latter is a market where Nvidia dominates, with nearly 99% of the market share. As Intel makes inroads here, AMD and Nvidia stand to lose out.

Based on its current portfolio and near-term roadmap, Intel is set to directly compete in the areas that AMD and Nvidia have historically dominated. And it is likely to capture the training and inference performance-per-watt crown from Nvidia soon with Nervana. Based on Intel’s historic ability to dominate its “big core” compute market categories, its expansion into areas once dominated by others should concern the competition.

Nvidia and AMD, meanwhile, have relatively narrow product portfolios. Even in cases where a customer prefers AMD for server chips or Nvidia for discrete GPUs, there’s still a considerable chance Intel will find its way into the mix via its storage or networking products. Intel also brings a platform approach to every market, so it wins even where some piece-parts may not be as competitive.

Better marketing resources and a solid refocus

AMD likes to tout its strengths in server chips, but if it is so good, why does it only have ~3% of the market? (And that 3% share comes despite the fact that Intel had delays launching its 10nm solutions.) While a significant amount of AMD’s server marketing budget seems to go toward negative campaigning against Intel, the company isn’t even halfway to where it sought to be in terms of server market share. At best, AMD has made small gains in the PC market, which is an area that’s only about half as profitable as the server side.

Nvidia has suffered similar setbacks in some of its supposed areas of strength, including the loss of Tesla as one of its marquee autonomous driving customers. Currently, Nvidia’s pivot strategy is to try to compete in the ADAS (Adaptive Driver Assistance Systems) space, which is Intel Mobileye‘s sweet spot and is an area where Qualcomm is also planning to compete. Qualcomm took out most of Nvidia’s cockpit wins, so Nvidia has nowhere to go. And the greatest challenge to Nvidia is that most inference is still done on Intel Xeon CPUs rather than Nvidia silicon, and Intel had Amazon on stage to reinforce this for the second year in a row at its Xeon Scalable launch.

Intel also has the greatest brand equity of the three companies and the deepest pockets for marketing investment, which is one reason the company does so well in markets even if it doesn’t have the “best chip.” Intel’s recent exit from the 5G modem business will allow it to refocus on its datacenter business, which should only make it harder for Nvidia and AMD to defend against market share loss. Even if Intel takes 5% of the discrete GPU and ML accelerator space from Nvidia and AMD, it will be a crushing defeat.

Total available market

Intel’s more diverse portfolio, mentioned above, also means it has a significantly larger total available market. Estimated at more than $ 330 billion at its analyst day, this market is more than 4x larger than the $ 74 billion market AMD shows in its current investor deck. Nvidia doesn’t break out its total addressable market (TAM) in its investor presentations as clearly, but it does provide forward-looking market sizing out to 2023 for datacenter and to 2025 for automotive, showing those to be at $ 50 billion and $ 30 billion, respectively. That’s a far cry from Intel’s figure. And for anyone who wants to make a big deal out of the exit from 5G modems, the TAM for that business is a mere $ 10 billion and wasn’t even included in the $ 330 billion TAM that Intel is currently planning to address.

In the end, it really isn’t that hard to see why Intel is the best positioned for growth. Its much larger product portfolio and total addressable market, together with its historic ability to dominate the compute market categories where it focuses, means Intel will be able to take market-dominating positions in its focal categories. And, in the near future, those categories will constitute most, if not all, of the places AMD and Nvidia play.

Graphics aren’t a weakness, but Intel is set to get stronger

It’s perhaps a little-known fact that Intel is actually the #1 market share leader today in PC graphics thanks to its lead in integrated graphics. Intel has historically ceded the discrete PC gaming graphics card market to AMD and Nvidia but is planning a competitive offering. Slated for release in 2020, the Intel Xe family will run the gamut from integrated graphics to discrete client graphics cards to discrete datacenter cards. If Intel delivers strong enough performance against the current competition, it could chip away at the market share Nvidia and AMD currently own. It would be devastating to both if Intel takes just 5% of this market in its first year.

Nanometers apart, and it doesn’t really matter

There’s been a general obsession about processor specs — 7nm, 10nm, 14nm, and so on. But it’s useful to reset expectations on that a bit. All the chip analysts, including us, equate TSMC 10nm with Intel 14nm+. Intel 14nm++ demonstrates superiority on cache cell size and density advantages over TSMC 10nm. Experts also equate TSMC 7nm with Intel 10nm on cache cell size and density.

Intel will have 10nm notebooks in Q3 2019, and AMD is talking about having 7nm notebooks in volume in Q1 2020. I’m confident, though, that AMD will not have mass availability for six months or so after the announcement. And if, as expected, TSMC 7nm and Intel 10nm have reasonable parity, then there’s no meaningful advantage either way.

The worst is history

I firmly stand by my assessment and Intel’s strong position going forward. I believe it’s important to take the past 12-24 months at Intel, and the challenges that the company has faced, into consideration. The negativity that has recently surrounded the company is certainly not baseless. The past two years have seen a lot, from an unexpected CEO change to chip shortages to Spectre/Meltdown to delays in its 10nm rollout. Yet all the while, Nvidia and AMD have had these Intel flaws to their advantage but have made only nominal market gains in their wake. This holds especially true in the datacenter, where AMD sought to gain significant ground in server chips but barely made a dent.

Now, with the 10nm delays reaching an end and a datacenter product roadmap that directly competes for market share in areas where Intel has never competed before, the company’s prospects are looking up. Going forward, Intel’s much larger, more diverse product portfolio and total addressable market, coupled with its historic ability to dominate the compute market categories where it focuses with a massive marketing budget, means it’s poised to achieve strong market positions in its focal categories. And, in the near future, those categories will constitute most, if not all, of the places AMD and Nvidia play.

Daniel Newman is the principal analyst at Futurum Research, which provides research, analysis, advising, and/or consulting to high-tech companies in the tech and digital industries. The firm doesn’t hold any equity positions with any companies cited.

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Big Data – VentureBeat

Intel is preparing for a data-centric tsunami

April 4, 2019 Big Data

Navin Shenoy, executive vice president at Intel, introduced more than 50 new products yesterday at Intel’s “data-centric” event in San Francisco. They range from the second-generation Xeon Scalable flagship processor to the Optane memory chips that will dramatically improve the capacity and density of data storage.

Shenoy said those products are necessary to feed the beast of demand for cloud-based services, from Netflix movies on demand to sensor analysis for self-driving cars.

The trends fueling the data-centric world include a proliferation of cloud computing, the growth of AI and analytics, and cloudification of the network and the edge. In the past five years, Intel saw a 50 percent increase in compute demand, and it predicts the same again in the next five years.

The demand for diverse workloads is increasing. So Intel has been investing to move data faster with Ethernet and silicon photonics, store more with Optane products, and process everything with CPUs, FPGAs, and custom chips. And for once, Intel isn’t trickling out its products. It’s launching them all at once. I talked with Shenoy at the event.

Here’s an edited transcript of our interview.

Navin Shenoy: You heard a lot from us today. The general high-level view of the company is we’ve been transforming for three or four years now. Hopefully you saw today’s announcements reflecting that transformation. Architecting the future of the data center and the edge requires, we think, a broader approach: move data faster, store more data, process everything. We’ve obviously, for a number of years now, built out that portfolio, but we never tried to bring it all together. That’s what today was about.

We’re on a journey. I told my team today, “Welcome to the starting line.” We’re on a journey to get after solving customer problems in moving data faster, storing more data, and processing everything. Today’s the first step.

Intel is preparing for a data centric tsunami

Above: Navin Shenoy of Intel says 50% of all data was created in the last two years.

Image Credit: Dean Takahashi

VentureBeat: Why does today feel different from, say, product launches that happened six months or a year or two years ago?

Shenoy: Historically we would have introduced seven new products at seven different events at seven different times. We would have had our individual product teams talking about the virtues and benefits of their products in isolation. Two or three years ago we started to recognize that, if you start from the workload and work your way backward – if you start from the customer problem and work your way backward – you can’t do that. You have to think about the problem holistically and try to solve it holistically. You miss opportunities if you don’t think about it that way.

The Twitter example today is a great example. I saw Matt talk about this privately at an event last summer. I asked him if he would come today and talk about how they found a bottleneck that they didn’t realize they had, by introducing the caching tier in their infrastructure with NAND. And how that then transformed the way they thought about compute resources. They didn’t realize there were storage bottlenecks, and they couldn’t take advantage of the CPU infrastructure. It’s a great example of reduction in TCO, reducing the footprint in the data center, and improving performance by using a higher-end CPU and introducing a new cache. You can imagine we’re working together with them on further ways to re-architect things as we think about the future.

We’re having that conversation hundreds of times a week with hundreds and thousands of customers. If you really want to figure out how to architect the future of the data center and the edge, you have to do it holistically, end to end, from the interconnect to the memory and storage to the compute.

VentureBeat: Have you updated your revenue number as far as where AI revenue for the company is?

Shenoy: No, we haven’t. We disclosed $ 1 billion in 2017. We haven’t updated it since. You can imagine that it’s growing fast. You can imagine that it’s growing faster than the baseline revenue of the company. But that’s as far as I want to go on that.

Above: Navin Shenoy of Intel says 50% of all data was created in the last two years.

Image Credit: Dean Takahashi

VentureBeat: How well are you doing against Nvidia on the training side of things?

Shenoy: First of all, I’d just say—one thing that’s important to know is that inference and training are going to evolve over time. Today they’re roughly 50/50. As I forecast where things are going, three to five years from now, inference is going to be an order of magnitude higher, multiples higher than training in terms of the amount of the compute workload that happens in the data center and at the edge.

That’s why, first and foremost, you’ve heard us talk a lot about embedding AI inference capability into Xeon, and why we’ve talked about building a discrete inference accelerator. That will come out in 2020. That’s why we acquired Movidius to do edge inference for low-power domains, and why we’re using FPGAs for low-latency inference in the data center as well. It’s important to recognize that inference is where the action is going to be over time.

On training, we have a portfolio coming out in 2020. We’re making progress on that portfolio. You’ll hear more about that portfolio from us as we get closer. But it’s really happening in 2020.

VentureBeat: Does that include a GPU?

Shenoy: Yes. We’ve said 2020, and as we get closer you’ll hear more from us on that.

VentureBeat: As far as the investment level that’s happening on the inference side or on the training side, how would you compare that? Are equal amounts still going into both, or will it be very different?

Shenoy: We’re going to invest consistent with market opportunity. On inference, while it’s not well-known, most AI inference happens on CPUs today. You saw today that we’re not resting on our laurels. We’re continuing to push on innovation. Adding DL Boost to Xeon is akin to adding MMX to Pentium way back in the day. What’s happening with the workload—we’re embedding it into the most volume data center CPU in the industry and unleashing all sorts of capabilities that people haven’t even dreamed about. At the same time, we parallel invest in discrete inference accelerators. That’s well underway, and probably our most significant investment.

But training is important too. We’re going to invest to participate in that part of the market. We don’t think we have a monopoly on all the best ideas, by the way. You’ve seen us invest in the startup ecosystem for AI. We invested in a company called Habana in Israel. Yesterday we announced an investment in a company called SambaNova here in Silicon Valley. We have a broad portfolio approach.

AI is already the fastest-growing workload in the data center, and it will be the fastest-growing workload at the edge. It’ll be part and parcel of everything we do. Three, four, five years from now we won’t talk about AI as its own thing. It’ll be like the way we used to talk about the internet in the late ‘90s. What is this internet thing? And then five years after that you didn’t talk about the internet. It’s just part of everything you do. I believe AI will be similar.

Above: Navin Shenoy runs the data center group at Intel.

Image Credit: Dean Takahashi

VentureBeat: Google’s Stadia cloud gaming project raised a lot of eyebrows. Does that seem like just one thing that’s happening that’s going to come along and create much more demand on the data center side?

Shenoy: The idea that media of all types are going to be increasingly delivered on demand, in interactive fashion, is not a new idea. Netflix, or ByteDance, or Facebook Live, or any kind of online gaming—this is just an evolution of the idea that people want to be able to consume all different types of media in a scalable fashion online. The infrastructure has to be built in a way to handle unpredictable surges.

I was talking to Twitter earlier about how—it’s very difficult for them to predict what the next surge is going to be. They know that, say, the World Cup is coming, so there will be a lot more tweets. But they don’t know when every event is going to occur that causes a surge. The only way you can handle that is by building an agile, flexible infrastructure. I don’t think online gaming, cloud gaming, is any different from that. It’s a similar phenomenon. It’s a hit-driven business. You never know when a game is going to take off. You have to build infrastructure in a flexible way to handle that.

VentureBeat: It still seems like the quality of the network, wherever you are, is going to determine what you get.

Shenoy: For sure. Technology is very unevenly distributed in the world. In my opinion, there’s always going to be a heterogeneous set of solutions for things that require low latency. This is why I talk a lot about compute moving closer to where the data is being created and consumed. There’s a reason why, through evolution, the sensors of the human being, eyes and ears and nose, are close to the compute. I think the computing world will evolve in a similar way. Computing will be closer to the sensors, the place where data is being created and consumed.

It’s inevitable that there’s going to be a massive buildout of compute closer to the user. I don’t think you’re ever going to see a world where all the compute sits in a faraway data center and a little bit of compute sits on your body. There’s going to be compute distributed throughout the network. I think that’s only going to accelerate as we move to 5G, which is a real profound shift. Industry observers will probably overestimate that in the short term, but underestimate it in the long term.

VentureBeat: Is there another way to translate how significant the 50 products introduced today will be to consumers?

Shenoy: It’s a fundamental transition for us, foundational transition for us. It’s going to be the fastest-ramping Xeon we’ve ever introduced. It has impact on the world in a way that’s difficult for us to imagine and difficult for us to predict. As you heard in some of the numbers being quoted today, the IT industry is at least a trillion dollars today. Digital transformation is this buzzword cliché that’s now moving from being two words on a page to being foundational to the way companies think about staying competitive, industries think about evolving, and economies and nations think about being competitive.

It’s difficult for me to put a number on it, but for an industry that’s a trillion dollars, something so foundational is going to have a major impact on the world. It’s what gets me excited. It’s why I drive into the office relatively quickly in the morning, because not only do we get to innovate, but we get to do it at scale and make a big impact on the world. I know my team’s excited about it. Our engineers are excited about it. Our customers are excited about it too.

VentureBeat: If this is your fastest ramp, does that mean your manufacturing issues are out of the way now?

Shenoy: On Xeon we haven’t had any challenges. We’ve prioritized Xeon. We haven’t constrained our customers’ growth at all. We’re ready to go. We’re going to ramp this as fast as we’ve ever ramped any product in our history.

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Intel Capital invests $117 million in more than a dozen startups

April 1, 2019 Big Data

Intel Capital today announced an investment of $ 117 million in 14 startups doing things like making AI inference faster, helping manufacturers deploy AI systems, building semiconductors, and creating disruptive tech beyond AI in health care and communications.

The companies hail from China, Canada, Israel, the United Kingdom, and the United States.

Companies supported as part of the announcement include Landing AI, a company created by former Google Brain cofounder Andrew Ng that helps manufacturers apply AI to their business practices, and Untether AI, which makes chips specifically for neural net inference.

An Intel Capital spokesperson declined to share specific amounts invested in each startup. Intel Capital routinely invests between $ 300 million and $ 500 million annually in startups creating unique, forward-thinking, or disruptive solutions.

Other AI startups that received backing today include:

Cloudpick, based in Shanghai, which uses deep learning and computer vision for ecommerce solutions.
Medical Informatics, based in Houston, which uses machine learning and data to bring predictive insights to patient care.
Zhuhai Eeasy Technology, based in Zhuhai, China, which makes AI systems for media-related use cases for deployment on the edge, such as video encoding and image processing.

While the majority of the 14 companies being supported by Intel Capital are creating products and services related to artificial intelligence, other companies are in different fields, such as Polystream, which delivers 3D applications for video games in the cloud; Pixeom, which helps clients manage large-scale hybrid cloud and edge computing; and Tibit Communications, which makes a device to improve Ethernet internet connections.

The news was shared today at Intel Capital’s Global Summit, a gathering for venture capitalists and entrepreneurs now in its 19th year, which is taking place this week in Phoenix, Arizona.

Intel also announced today at Global Summit an investment in hbcu.vc, a nonprofit organization that helps students from historically black colleges and universities, as well as Hispanic students, learn more about venture capital and entrepreneurship.

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Intel reveals Hewitt Lake processor family for internet of things

February 25, 2019 Big Data

Contrary to popular belief, there’s more to the conference formerly known as Mobile World Congress than smartphones. Take Intel’s MWC Barcelona announcements, for example — they’re a bit more infrastructural in nature. The Santa Clara chipmaker today debuted a new system-on-chip for edge devices and a field-programmable gate array (FPGA) — the N3000 Programmable Acceleration Card (PAC) — designed to juggle network traffic up to 100Gbps.

The abovementioned edge chip, code-named “Hewitt Lake,” slots into Intel’s existing Xeon D 64-bit multicore microserver family, and promises improvements to both power efficiency and performance for network edge and storage solutions. It boasts a faster base frequency than Xeon D-1500 NS — a 14-nanometer product family launched in 2015 — and while details were hard to come by at press time, Hewitt Lake will presumably feature an integrated platform controller hub (PCH), a ball grid array (BGA), and other features comparable to the current-gen chip lineup.

Meanwhile, FPGA Programmable Acceleration Card (PAC) N3000, dubbed “Vista Creek,” is a 1/2 length, full-height, dual-slot PCIe card that’s optimized for custom vRAN and core solutions. Its internal hardware — which comprises 1.1 million logic elements, 9GB of DDR4 memory and 144MB of QDR IV memory, and two XL710 network adapters for packet processing — drives virtualized workloads up to 8 x 10Gbps and 4 x 25Gbps, Intel says. The company is pitching it as a processing solution for 5G radio access networks, core network apps, and more.

The N3000 is expected to be available in the third quarter of 2019.

In addition to the Hewitt Lake and the N3000, Intel today revealed that it would partner with Ericsson and ZTE to embed Intel’s Snow Ridge chip in the former two companies’ 5G base station designs. It also said that it’s working with Skyworks to optimize the 5G RF solution for its XMM 8160 modem (ahead of the projected 2020 release date), and with Fibocam, a M.2 module manufacturer, to build its 5G modem into Fibocam’s forthcoming FG100 product.

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Tag Archives: Intel

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