Intel Archives - Business Intelligence Info

Tag Archives: Intel

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.

Let’s block ads! (Why?)

Big Data – VentureBeat

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.”

Let’s block ads! (Why?)

Big Data – VentureBeat

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.”

Let’s block ads! (Why?)

Big Data – VentureBeat

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.

Let’s block ads! (Why?)

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.

Let’s block ads! (Why?)

Big Data – VentureBeat

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.

Let’s block ads! (Why?)

Big Data – VentureBeat

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.

Let’s block ads! (Why?)

Big Data – VentureBeat

Intel and Tencent debut AI-powered camera systems for retail

November 2, 2018 Big Data

Tencent and Intel are teaming up to launch a pair of artificially intelligent (AI) products for retail, the two announced today during Tencent’s Global Partner conference in China. Both were developed by Tencent’s YouTu Lab, its computer vision research division, and have Intel’s Movidius Myriad chips inside.

The first is DeepGaze, an AI-powered camera for brick-and-mortar that keeps tabs on shoppers’ movements. It can track the number of customers near a given shelf display at various times throughout the day, and perform hybrid object detection — some on-device, and the rest in Tencent’s Intel Xeon Scalable processor-based cloud.

DeepGaze sports the Movidius Myriad 2 vision processing unit (VPU), the same chip inside Google’s Clip camera, Flir’s Firefly, and DJI’s Phantom 4 drone. It’s optimized for image signal processing and inference — the point at which a trained AI model makes predictions — on-device.

“With artificial intelligence, enterprises can gain new insights about their customers to both elevate the users’ experience and drive business transformation,” Remi El-Ouazzane, vice president and chief operating officer of Intel’s AI Products Group, said. “Tencent’s new … [solution] take[s] advantage of powerful Intel … chips to enable deep neural networks to run directly on the cameras, providing real-time and actionable data for various businesses, including retail and smart buildings.”

Intel and Tencent debut AI powered camera systems for retail

Above: The DeepGaze camera.

Image Credit: Intel

DeepGaze compliments the YouBox, also announced today. It’s an on-premises server similarly designed for retail that, with the help of onboard AI systems, can ingest real-time feeds from up to 16 cameras and derive useful insights. Store owners can use it to predict sales performance and product turnover, Intel and Tencent said, enabling them to restock shelves without the need for manual inventory management.

Under the hood is the Movidius Myriad X vision processing unit (VPU), which features a dedicated hardware accelerator for AI computations.

“Intel is the perfect partner for our flexible enterprise solutions,” Simon Wu, general manager at Tencent’s YouTu Lab, said. “Based on Intel Movidius Myriad chips and VPUs, the YouTu camera and box perform inference at the edge in tandem with Intel Xeon Scalable processors in the cloud to provide cost-effective and flexible solutions for verticals including retail and construction.“

Above: The YouBox.

Image Credit: Intel

Tracking customers in the real world

With DeepGaze and YouBox, Tencent and Intel are dipping a tepid toe in an increasingly lucrative market: AI-driven retail analytics. They’re not the first.

In June, Japan telecom company NTT East collaborated with startup Earth Eyes to create AI Guardsman, a machine learning system that attempts to prevent shoplifting by scanning live camera feeds for suspicious activity.

Firms like Standard Cognition and Trigo, meanwhile, are leveraging machine learning to build cashierless, data-rich shopping experiences in physical stores.

Tel Aviv-based Trigo, like AI Guardsman, taps a network of cameras to track customers through isles, automatically tabulate their bills, and surface coupons and other engagement opportunities. Standard Condition — which opened a location in San Francisco last month — offers its retail partners and the customers who shop with them a comparable AI-driven solution, as does Zippin, which also debuted a checkout-free store in the Bay Area recently.

Amazon’s the elephant in the room. Its Amazon Go store chain employs sensors, AI, and smartphones to streamline retail flows, and it’s reportedly bent on nationwide expansion: Bloomberg reported in September that it plans to open as many as 3,000 locations by 2021, up from the four operating today.

Even Microsoft’s said to be working on cashierless store technology.

AI-first strategies

Both Tencent and Intel see AI as a key part of their respective growth strategies.

Tencent’s AI funding arm is one of the largest of its kind; the company has poured more capital into startups and AI chips than its biggest Chinese rivals, Baidu and Alibaba. One of its largest single investments is in robotics startup UBTech, which aims to develop a humanoid robot capable of walking downstairs and autonomously navigating unfamiliar environments.

In 2017, Tencent opened an AI research lab in Bellvue, Washington led by Dr. Dong Yu, a former Microsoft engineer and pioneer in speech recognition tech. (Its other AI lab is based in Shenzhen.) And its YouTu Lab, which recently open-sourced some of its developer tools, is working customers like China Unicom and WeBank on facial ID authentication.

For Intel’s part, partnerships with OEMs like Tencent are a step toward its ambitious goal to capture the $ 200 billion AI market.

In August, it bought Vertex.ai, a startup developing a platform-agnostic AI model suite, for an undisclosed amount. Meanwhile, the chipmaker’s acquisition of Altera brought field programmable gate array (an integrated, reconfigurable circuit) into its product lineup, and its purchases of Movidius and Nervana bolstered its real-time processing portfolio.

Of note, Nervana’s neural network processor, which is expected to begin production in late 2019, can reportedly deliver up to 10 times the AI training performance of competing graphics cards.

“After 50 years, this is the biggest opportunity for the company,” Navin Shenoy, executive vice president at Intel, said at the company’s Data Centric Innovation Summit this year. “We have 20 percent of this market today … Our strategy is to drive a new era of data center technology.“

Let’s block ads! (Why?)

Big Data – VentureBeat

Why Renee James is challenging Intel with ARM-based server chips

February 8, 2018 Big Data

I met Renee James more than a couple of decades ago, when she was the technical assistant to then Intel CEO Andy Grove, one of the legendary leaders of Silicon Valley. She kept notes on what she learned from Grove, and she rose through the ranks to become president of the world’s biggest chip maker. But she left Intel and the spotlight in February 2016.

And now she’s back, as the CEO of Ampere, a new ARM-based chip company financed by the Carlyle Group. Ampere has hundreds of chip designers making server chips based on the ARM architecture that powers the world’s smartphones. The company promises to give Intel some serious competition in the sweet spot of the server market. The ARM-based chips have 32 cores and will run at speeds above 3 gigahertz on 125 watts of power. It’s a serious effort to provide an alternative form of computing in data centers with different kinds of work loads.

It’s a very ambitious plan, but James said in an interview with VentureBeat that she drew inspiration from her former mentor, Grove, before he passed away in March 2016.

“I remember every word he ever uttered,” she said. “In fact, he and I discussed this before he passed away. He said to me, ‘There’s a lot of easier businesses in the world than this one, but this is worthy.’ I remember everything he taught me, or I wouldn’t be doing this.”

James, who after leaving Intel took a job at the investment company Carlyle Group and is now heading Ampere, isn’t going head-on against Intel, which has 99 percent of the server chip market according to market researcher IDC. But she believes the market is ready to embrace alternative processors for certain kinds of workloads. The chips are expected to hit the market in the second half of this year.

Here’s an edited transcript of our interview.

Why Renee James is challenging Intel with ARM based server chips

Above: This is the first prototype from ARM-based processor designer Ampere.

Image Credit: Ampere

Renee James: The company is named Ampere. We’re a new semiconductor company building hyperscale cloud processors to be used in storage as well as servers, based on ARM 64. We’re an ARM licensee, so we’re building our own cores and our own designs. Our first product is in sample now and out in the system. People are working on it. We have a presentation coming, in which you’ll see all of the workloads and software that are already running on it. It’s a 3-Ghz part, 125 watts, so it has excellent total cost of ownership (TCO), excellent price performance.

VentureBeat: I was just visiting some folks at XO, and they said, “You won’t believe it, but we’re seeing a lot of semiconductor startups.”

James: It’s a funny thing. For 10 years there were none. In the last couple of years there have been a bunch of accelerator chips that have gotten different levels of funding. You haven’t seen many CPU companies. When I looked into it and I was talking to VCs and raising money, I realized that there’s a lot of activity and innovation around accelerators for different workloads, but there aren’t a lot of people doing new CPUs. I think everyone in the industry knows that Broadcom sold theirs off. Others have gotten in and out. We’re pretty unique. We’re building microprocessors for servers.

VB: How long ago did you guys get started?

James: I finalized the acquisition about three or four months ago. We’ve been thinking about it and raising money for six to eight months. I acquired a team with a partially finished third-generation design, and I hired the impact engineering team, almost 300 people. I’ve hired a lot of people since then. We’re well on our way. The product is sampling, so it’s out. We’re working on the next design and the design after that, so we’ve brought on a second design team. We have quite a road map planned.

VB: How much of the time since then has gone into this project? Is it just the past year or so, or less?

James: I left on February 1, 2016. It was announced earlier. I worked for the Carlyle Group in private equity. I’ve worked for them for two years. They’re the backers of this, the primary backer, although there are other investors. I started working on this probably inside of 2017. 2016 and the first part of 2017 I was doing other deals and things for Carlyle as an operating executive.

VB: How much has been invested in this? Is it a new investment that you’re leading?

James: It’s a new investment, yes. We’re not talking about the amount. If you travel around the valley and ask people how much money they invest in semis, they’ll tell you anything from nothing to not that much. To do this kind of work, it takes a tremendous amount of investment. We raised quite a bit of money.

Above: Ampere’s target markets

Image Credit: Ampere

VB: So it’s more like private equity money than venture capital money.

James: Right. ARM licenses are more than most VCs give in a round. It’s a PE-backed startup.

VB: Where did this team come from?

James: There was an announcement that Carlyle had acquired assets out of Macom. Macom had this team that was working on this, and it wasn’t a business they wanted to go into. It was a team they’d inherited through a different acquisition. We’d been looking at it. When I decided I wanted to start the company I looked at a bunch of different teams that were available, technologies I could purchase to get started. This was one of them. Macom is a semiconductor company out of Boston that has a lot of networking and other assets. They’re public.

VB: Were they already doing a CPU, or were they doing something else?

James: It’s a long history, but the team had been part of a company that did network chips. They had started thinking about doing CPUs, a bit like what Cavium is trying to do, and Marvell, the new merger, from that point of view. Macom bought the entire company, and they said, “We only want to do networking. We don’t want to do CPUs. We don’t understand that business and it’s too much money.” They were looking to sell that off, and we bought that. We were looking at other CPU teams that were available in the Valley, coming from other large companies.

VB: The company that Macom bought, was that Applied Micro?

James: Right. Those guys work for me now. They’ve been on a very long journey, but now they’re out of the dark. In addition to those guys, we’ve hired people from Apple, people from Intel, people from AMD. We have a senior fellow from AMD. We have a really interesting group, a lot of really talented folks. If you knew the Applied Micro guys, you know they’re talented.

Above: Andy Grove, former CEO of Intel.

Image Credit: Intel

VB: It’s been a long time since you were helping Andy Grove out there. Did you remember anything he said?

James: I remember every word he ever uttered. In fact, he and I discussed this before he passed away. He said to me, “There’s a lot of easier businesses in the world than this one, but this is worthy.” I remember everything he taught me, or I wouldn’t be doing this.

VB: How did it feel to wrap your head around competing with the likes of Intel?

James: I don’t actually look at it that way. I look at it as investing in what comes next. We’re not building quantum computing. We’re not building high-performance computing. We’re not building legacy enterprise. We’re not building PCs. The list of things I’m not doing that Intel does it long. Intel is the best at what they do and no one knows that better than I do. I would never start a company and raise this much money to go do what Intel does. That’s ridiculous.

I’m doing what I think comes next. I think this is the next platform for the cloud. I’ve had people say, “But it’s too late. It’s already done.” I have to remind them that I worked on the project to make Pentium Pro into an enterprise product when everyone told me that enterprise was done. “It’s done. We use Sun. We use DEC. It’s done.” Not so done, actually? I don’t think we’re over. I think we get to invent the next thing. It’s only over until it isn’t. You know who that sounds like.

VB: Where are you based?

James: Santa Clara, Great America Parkway. We have people in Bangalore, Vietnam, and Portland, Oregon as well.

VB: What is the headcount more like now, beyond that 300?

James: Oh, yes. We’re not 20 people in a garage, even though we’re a startup.

VB: ARM-based servers, can you talk more about that opportunity? It seems like it went up a hype curve and then leveled out, maybe took a dip. Are you feeling like it’s back on track?

James: There was a lot of hope, and it might have been a little premature. Up until this generation of technology, it really wasn’t, I would say, at parity for the enterprise. It wasn’t 64-bit. It didn’t have a lot of the features you need. What’s new now is that the ARM architecture has evolved. The other part is that you have to get it built somewhere. You need a high-performance process and high-performance transistors. Not everyone can build those. We use [contract chip manufacturer] TSMC. It’s a 16-nanometer TSMC part. Our next part will be a 7-nanometer TSMC part.

There’s been a lot of evolution in established semiconductor manufacturing in the last five years. If you look at the hype curve on ARM for the last five to eight years, I agree with you. You’re always thankful to those that came before you, even if some of them died off. They were pioneers. We’re at a point where parity has been achieved, so it’s feasible.

The other thing is, the software ecosystem has completely changed in the last several years. If you were building an ARM part to go into legacy enterprise with a calcified legacy software stack, that’s a tough row to hoe. If you’re building a new part with a new approach to memory and bandwidth and throughput for cloud workloads like in-memory databases and Java and containers and AI, that’s a very different machine. You get to start fresh. It’s fun, right? Every day it’s the beginning again, when we first started building processors for the enterprise.

12View All

Let’s block ads! (Why?)

Big Data – VentureBeat

Intel and Waymo collaborate on self-driving compute platform

September 19, 2017 Big Data

(Reuters) — Intel Corp on Monday announced a collaboration with Alphabet’s Waymo self-driving unit, saying it had worked with the company during the design of its compute platform to allow autonomous cars to process information in real time.

The world’s largest computer chipmaker said its Intel-based technologies for sensor processing, general compute and connectivity were used in the Chrysler Pacifica hybrid minivans that Waymo has been using since 2015 to test its self-driving system.

“As Waymo’s self-driving technology becomes smarter and more capable, its high-performance hardware and software will require even more powerful and efficient compute,” said Intel Chief Executive Brian Krzanich in a statement announcing the ongoing collaboration.

Intel, which announced the $ 15 billion acquisition of autonomous vision company Mobileye in March, is pushing to expand its real estate in autonomous vehicles, a fast-growing industry. A collaboration with Waymo, considered by many industry experts to be at the forefront of autonomous technology, adds to its portfolio.

The announcement marked the first time Waymo, formerly Google’s autonomous program, has acknowledged a collaboration with a supplier. The company has done most of its development work in-house.

Intel began supplying chips for then-Google’s autonomous program beginning in 2009, but that relationship grew into a deeper collaboration when Google began working with Fiat Chrysler Automobiles (FCHA.MI) to develop and install the company’s autonomous driving technology into the automaker’s minivans.

Waymo, which has developed its own sensors, is not using the autonomous vision system created by Mobileye.

Underscoring the non-exclusive partnerships and collaborations in the space, Mobileye and Intel are in an alliance with German automaker BMW (BMWG.DE) and Fiat-Chrysler to create an industry-wide autonomous car platform.

Waymo CEO John Krafcik said fast processing was crucial to the performance of its autonomous vehicles.

“Intel’s technology supports the advanced processing inside our vehicles, with the ability to manufacture to meet Waymo’s needs at scale,” Krafcik said in a statement.

Let’s block ads! (Why?)

Big Data – VentureBeat

Tag Archives: Intel

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

Intel researchers propose AI that recognizes faces from thermal images

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

Why Intel is well-positioned against AMD and Nvidia

A stronger portfolio and ecosystem

Better marketing resources and a solid refocus

Total available market

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

Nanometers apart, and it doesn’t really matter

The worst is history

Intel is preparing for a data-centric tsunami

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

Intel reveals Hewitt Lake processor family for internet of things

Intel and Tencent debut AI-powered camera systems for retail

Tracking customers in the real world

AI-first strategies

Why Renee James is challenging Intel with ARM-based server chips

Intel and Waymo collaborate on self-driving compute platform

Recent Posts

Categories

Archives