The Core Technologies of Deep Learning

The Core Technologies of Deep Learning

By Unknown   06:30   No comments:

When the movie The Terminator was released in 1984, the notion of computers becoming self-aware seemed so futuristic that it was almost difficult to fathom. But just 22 years later, computers are rapidly gaining the ability to autonomously learn, predict, and adapt through the analysis of massive datasets. And luckily for us, the result is not a nuclear holocaust as the movie predicted, but new levels of data-driven innovation and opportunities for competitive advantage for a variety of enterprises and industries.

Artificial intelligence (AI) continues to play an expanding role in the future of high-performance computing (HPC). As machines increasingly become able to learn and even reason in ways similar to humans, we’re getting closer to solving the tremendously complex social problems that have always been beyond the realm of compute. Deep learning, a branch of machine learning, uses multi-layer artificial neural networks and data-intensive training techniques to refine algorithms as they are exposed to more data. This process emulates the decision-making abilities of the human brain, which until recently was the only network that could learn and adapt based on prior experiences.

Deep learning networks have grown so sophisticated they’ve begun to deliver even better performance than traditional machine learning approaches. One advantage of deep learning is that there is little need to "train" the system and define features that might be useful for modeling and prediction. With only basic labeling, machines can now learn these features independently as more data is introduced to the model. Deep learning has even begun to surpass the capabilities and speed of the human brain in many areas, including image, speech, or text classification, natural language processing, and pattern recognition.

The core technologies required for deep learning are very similar to those necessary for data-intensive computing and HPC applications. Here are a few technologies that are well-positioned to support deep learning networks.

Multi-core processors:

Deep learning applications require substantial amounts of processing power, and a critical element to the success and usability of deep learning comes with the ability to reduce execution times. Multi-core processor architectures currently dominate the TOP500 list of the most powerful supercomputers available today, with 91% based on Intel processors. Multiple cores can run numerous instructions at the same time, increasing the overall processing speed for compute-intensive programs like deep learning, while reducing power requirements, increasing performance, and allowing for fault tolerance.

The Intel® Xeon Phi™ Processor, which features a whopping 72 cores, is geared specifically for high-level HPC and deep learning. These many-core processors can help data scientists significantly reduce training times and run a wider variety of workloads, something that is critical to the computing requirements of deep neural networks.

Software frameworks and toolkits:

There are various frameworks, libraries, and tools available today to help software developers train and deploy deep learning networks, such as Caffe, Theano, Torch, and the HPE Cognitive Computing Toolkit. Many of these tools are built as resources for those new to deep learning systems, and aim to make deep neural networks available to those that might be outside of the machine learning community. These tools can help data scientists significantly reduce model training times and accelerate time to value for their new deep learning applications.

Deep learning hardware platforms:

Not every server can efficiently handle the compute-intensive nature of deep learning environments. Hardware platforms that are purpose-built to handle these requirements will offer the highest levels of performance and efficiency. New HPE Apollo systems contain a high ratio of GPUs to CPUs in a dense 4U form factor, which enables scientists to run deep learning algorithms faster and more efficiently while controlling costs.

Enabling technologies for deep learning is ushering in a new era of cognitive computing that promises to help us solve the world’s greatest challenges with more efficiency and speed than ever before. As these technologies become faster, more available, and easier to implement, deep learning technologies will secure their place in real-world applications – not in science fiction.

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Datacenter Efficiencies Through Innovative Cooling

Datacenter Efficiencies Through Innovative Cooling

By Unknown   04:04   No comments:

Datacenters that are designed for High Performance Computing  (HPC) applications are more difficult to design and construct than those that are designed for more basic enterprise applications.  Organizations that are creating these datacenters need to be aware of, and design for systems that are expected to run at their maximum or near maximum performance for the lifecycle of the servers. While enterprise datacenters can be designed for less server density and less heat generated per server due to the type of workloads, HPC centers must be designed for higher usage per server. For example, simulations in many domains may run at peak performance (depending on the algorithms) for weeks at a time, while enterprise applications may only need peak performance for short bursts, such as payroll computations.

Two main buckets of expenses are usually associated with the planning and implementation of a new datacenter, or the upgrade of an existing datacenter to accommodate the latest generation and performance of new server technology. The first and more generally understood one is the Capital Expense, or CAPEX for short. CAPEX is the amount that the organization will pay to purchase a new asset, especially a new physical asset. When creating or upgrading a new datacenter, the CAPEX is the amount paid for the computer servers, racks, storage systems, networking equipment, etc., which will usually be paid once, in the current fiscal year. Many organizations focus on this value in determining the cost of acquiring new systems. However, the recurring cost or Operational Expense (OPEX), over the life of the computer systems will traditionally be higher.  Leading data center operators both understand this and know that reducing OPEX can provide competitive advantage and release budget dollars for investment in more computing capacity.

Reducing OPEX

OPEX is the sum of all of the expenses that an organization will have to pay to keep the servers running. This includes, but is not limited to such items as electricity, cooling (which includes the electricity), construction financing (if new construction was required), and maintenance.

One of the main costs in operating a data center is the cooling of the servers. When servers that are being used for HPC applications are running at full utilization, the CPUs produce more heat, than when waiting for work to be done. While the performance per watt of CPUs has increased dramatically over the past few decades, HPC installations are built to deliver the maximum performance of the system to the end user.  Today’s modern two socket high end HPC servers can approach a 1,000 watt requirement.  The electricity required for this type of server (while needed for the server in order to run and perform as expected) also includes a significant requirement for the power that is needed in order to cool the servers. CPUs have an envelope of operating temperatures that must be met, or the CPU will likely fail, often with a cascading impact on cluster throughput and reliability.

Datacenter design has focused in recent years on how to place racks and racks of servers in order to isolate and remove the heat that is produced by the servers, mainly the CPUs. Most servers today have been designed to have high speed and redundant fans in the back of the server, so that cool air can be pulled over the CPUS and heat sinks in order to cool them. This results in designing the data center to have hot aisles and cold aisles. For example, two rows of racks of systems may sit back to back. The cooler air from the front of the system is drawn over the hot chips into the hot aisle, and then powerful exhaust fans pull this hot air away from the hot aisles, and cooler air is returned to the cold aisle. Significant expense is required to contain the hot air in the hot aisle and to remove and cool the hot air.

An alternative is to provide cooling of the CPUs much closer to the CPU itself.  If a significant reduction in the hot air that is produced is achievable, then less Computer Room Air Conditioning (CRAC) is needed, reducing OPEX expenses. In addition, higher densities of the servers can be achieved, as less hot air is produced by each server into a given space (the hot aisle). The monetary effect of reducing power consumption is directly related to the overall OPEX. For example, reducing the power consumption by 50 %, can lead to a reduction of 20 + % in total data center power savings.

Asetek In-RackCDU D2C 

Asetek specializes in liquid cooling systems for data centers, servers, workstations, gaming and high performance PCs. For HPC installations, Asetek RackCDU D2C™ (Direct-to-Chip) applies liquid cooling technology directly on the chip itself. Because liquid is 4,000 times better at storing and transferring heat than air, Asetek’s solutions provide immediate and measurable benefits to large and small data centers alike. The Asetek solution consists of a plate that is attached to the top of the processor and provides cold liquid to the chip itself. The hot liquid is then pumped away from the CPU where the liquid can then be chilled. This reduces almost all of the heat from the airflow of the server, reducing the CRAC requirements, which reduces the OPEX accordingly.

While the exact OPEX savings will vary depending on CPU loads, electricity costs, number of servers, and server density per rack, using Asetek cooling products can significantly reduce the OPEX costs for small and large data centers. Asetek has designed a simple calculator to assist in computing the cost savings. It is well worth investigating innovative chip and server cooling solutions in order to keep an HPC datacenter running and producing results faster than previous generations of systems.

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Bright Computing Powers HPC Cluster at Oldenburg University

Bright Computing Powers HPC Cluster at Oldenburg University

By Unknown   03:57   No comments:

Today Bright Computing announced that Oldenburg University in Germany has once again chosen to renew its license agreement with Bright Computing.

Oldenburg first became a Bright Computing customer in 2011, choosing Bright Cluster Manager to administer its small HPC environment. In recent years the Oldenburg HPC system has grown to a considerable 600-node cluster to provide an increasing amount of compute power to various departments within the university. With a small IT team and limited administration resources available, when it came time to upgrade the university’s IT hardware, the IT Services team at Oldenburg took the decision to continue using the Bright infrastructure management technology to overarch the HPC environment.

“There were three compelling reasons for Oldenburg to choose to reinvest with Bright,” said Dr. Stefan Harfst, Oldenburg University. “Firstly, Bright helps you to get your HPC environment up and running very quickly. Secondly, Bright makes it incredibly easy to manage your HPC environment which takes a lot of pressure of the IT Services team. Thirdly, Bright is a very robust and reliable, so our team is free to focus on other tasks.”

During the evaluation process, Oldenburg considered a number of independent infrastructure management technologies, as well as some open source software. However, the IT Services team chose Bright for its superior set of features and functionality, acknowledging that investing in a tool rather than deploying freeware was easily justifiable.

Haroon Ibrahim, Account Director for Oldenburg at Bright Computing, added; “By Standardising on Bright, Oldenburg is no longer tied to a single hardware vendor and can now install any hardware it likes. Added to this, Bright will enable Oldenburg to continue to scale its cluster, and in the future expand into new areas such as big data.”

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CloudLightning Report Looks at Barriers to HPC in the Cloud

CloudLightning Report Looks at Barriers to HPC in the Cloud

By Unknown   23:21   No comments:

The CloudLightning Project in Europe has published preliminary results from a survey on Barriers to Using HPC in the Cloud.

"Cloud computing is transforming the utilization and efficiency of IT infrastructures across all sectors. Historically, cloud computing has not been used for high performance computing (HPC) to the same degree as other use cases for a number of reasons. This executive briefing is a preliminary report of a larger study on demand-side barriers and drivers of cloud computing adoption for HPC. A more comprehensive report and analysis will be published later in 2016. From June to August 2016, the CloudLightning project surveyed over 170 HPC discrete end users worldwide in the academic, commercial and government sectors on their HPC use, perceived drivers and barriers to using cloud computing, and uses of cloud computing for HPC."

As shown in Figure 2, trust in cloud computing would appear to be a significant barrier to adopting cloud computing for HPC workloads. Data management concerns dominate the responses. This is not surprising given the large number of bio-science and university and academic respondents within the sample. The main technical barriers relate to communication speeds. This reflects a perceived lack of cloud infrastructure capable of meeting the communications and I/O requirements of high-end technical computing. Government policy is again ranked low it would seem it is neither a driver nor a barrier. Unsurprisingly availability and capital expenditure are not barriers reflecting their positive impact on adoption.

According to the report, there is unlikely to be a full shift of high performance computing workloads to the cloud in the short term however there is evidence of demand to meet the capacity limitations of internal infrastructures including use cases for testing the viability of the cloud or specific software for various use cases. This is consistent with previous research.

"Funded by the European Commission’s Horizon 2020 Program for Research and Innovation, CloudLightning brings together eight project partners from five countries across Europe. The project proposes to create a new way of provisioning heterogeneous cloud resources to deliver services, specified by the user, using a bespoke service description language. Our goal is to address energy inefficiencies particularly in the use of resources and consequently to deliver savings to the cloud provider and the cloud consumer in terms of reduced power consumption and improved service delivery, with hyperscale systems particularly in mind."

Download The Report

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TYAN HPC Platforms Add Support for NVIDIA Tesla P100, P40 and P4 GPUs

TYAN HPC Platforms Add Support for NVIDIA Tesla P100, P40 and P4 GPUs

By Unknown   23:00   No comments:

TAIPEI, Taiwan, Sept. 21 — TYAN, an industry-leading server platform design manufacturer and subsidiary of MiTAC Computing Technology Corporation, announces support and availability of the NVIDIA Tesla P100, P40 and P4 GPU accelerators with the new NVIDIA Pascal architecture. Incorporating NVIDIA’s state-of-the-art technologies allows TYAN to offer the exceptional performance and data-intensive applications features to HPC users.

“Real-time, intelligent applications are transforming our world, thus our customers need an efficient compute platform to deliver responsive and cost-effective AI,” said Danny Hsu, Vice President of MiTAC Computing Technology Corporation’s TYAN Business Unit. “TYAN is pleased to work with NVIDIA to market FT77C-B7079 and TA80-B7071 servers with P100, P40 and P4 to market. The TYAN NVIDIA-based server platforms allow hyper-scale customers to deploy accurate, responsive AI solutions, and to reduce inference latency up to 45x. The high throughput and best in class efficiency of Pascal GPUs make it possible to process exploding volumes of data to offer cost effective, accurate AI applications.”

“The NVIDIA Pascal architecture is the computing engine for modern data centers. Powered by Pascal, Tesla GPUs offer massive leaps in performance and efficiency required by the ever increasing demand of AI applications,” said Roy Kim, Tesla Product Lead at NVIDIA. “We’re partnering with TYAN to deliver the accelerated solutions customers need to deploy HPC applications and AI services.”

TYAN HPC platforms with support for NVIDIA Tesla P100, P40, P4

4U/8 GPGPU FT77C-B7079 – Support up to 2x Intel Xeon E5-2600 v3/v4 (Broadwell-EP) processors, 24x DDR4 DIMM slots, 1x PCI-E x8 mezzanine slot for high-speed I/O option, 10x 3.5″/2.5″ hot-swap SATA 6Gb/s HDDs/SSDs, dual-port 10GbE/GbE LOM, and (2+1) 3,200W redundant power supplies with 80-Plus Platinum rated.

2U/4 GPGPU TA80-B7071 – Support up to 2x Intel Xeon E5-2600 v3/v4 (Broadwell-EP) processors, 16x DDR4 DIMM slots, 1x PCI-E x8 slot for high-speed I/O option, 8x 2.5″ hot-swap SAS or SATA 6Gb/s plus 2x 2.5″ internal SATA 6Gb/s HDDs/SSDs, dual-port 10GbE/GbE LOM, and (1+1) 1,600W redundant power supplies with 80-Plus Platinum rated.

About TYAN

TYAN, a leading server brand of MiTAC Computing Technology Corporation under the MiTAC Holdings Corporation (TSE:3706), designs, manufactures and markets advanced x86 and x86-64 server/workstation board and system products. The products are sold to OEMs, VARs, System Integrators and Resellers worldwide for a wide range of applications. TYAN enable customers to be technology leaders by providing scalable, highly-integrated and reliable products such as appliances for cloud service providers (CSP) and high-performance computing and server/workstation used in CAD, DCC, E&P and HPC markets. For more information, visit MiTAC Holdings Corporation’s website at http://www.mic-holdings.com  or TYAN’s website at http://www.tyan.com

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ARM announces safety-first IoT processor for robots and cars

ARM announces safety-first IoT processor for robots and cars

By Unknown   23:07   No comments:

The Cortex R52 is coming to a robot near you - ARM announces safety-first IoT processor for robots and cars

MICROPROCESSOR DESIGNER ARM has announced a new chip for real-time safety-critical applications when humans come into contact with machines.

The Cortex R-52 has been five years in development and is engineered to meet new safety standards as ARM takes aim at the growing market of large-scale smart devices, such as surgical robots and self-driving cars.

Chip manufacturers see the safety-critical processor as an important growth market as the IoT moves more into the consumer realm. Intel scooped up Yogitech in April, an IoT startup focused on boosting the security credentials of chips used in robots, self-driving cars and other autonomous devices.

The new ARM chip can switch between tasks 14 times faster than its predecessor, the Cortex R-5, according to John Ronco, vice president of product marketing at ARM, who said that the design has already been commercially licensed to semiconductor firm STMicroelectronics.

Safety-critical chips are vital in situations where autonomous or semi-autonomous machines could cause injury or death in the event of a fault or a hack.

Vehicles are becoming increasingly dependent on software to optimise performance and make autonomous decisions, but one of the key problems holding back developments such as driverless cars is concern over how easily they can be hacked and the consequences of software bugs.

ARM claimed that the Cortex R-52 "delivers the highest level of integrated capability for functional safety" of any ARM chip so far.

"Cortex-R52 implements hardware to simplify the integration of increasingly complex real-time software environments while providing the robust separation of software necessary to protect safety-critical code," ARM said on its website.

"As the first ARMv8-R processor, Cortex-R52 introduces an extra privilege level which provides support for a hypervisor."

ARM unveiled the Cortex-A73 processor and Mali G71 CPU in May which it said will power the majority of virtual reality-ready smartphones in 2017.

Formerly the UK's biggest technology firm, ARM was recently acquired by Japan's SoftBank Group for £23.3bn. μ

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Intel's new PC, IoT chief brings fresh ideas to the veteran chip maker

Intel's new PC, IoT chief brings fresh ideas to the veteran chip maker

By Unknown   23:53   No comments:

Intel's second-in-command Venkata Renduchintala is feeling at home with his new company after he switched over from Qualcomm

 Venkata Renduchintala is president of Intel's Client and Internet of Things (IoT) businesses and Systems Architecture Group.

Intel is now more than just a PC company. At industry events, the company's keynotes feature drones flying around, robots walking on stage and musicians creating tunes from wearables. The chip maker is helping BMW build an autonomous car, will sell modems to Apple, and is leading the development of next-generation 5G cellular networks. For all these new markets, it will provide chip and data-center technologies.

The transformation is happening partly under the leadership of Venkata Renduchintala, president of the Client and Internet of Things (IoT) Businesses and Systems Architecture Group at Intel. As Intel's second-in-command, he helped cut struggling products like mobile CPUs and sharpened the company's focus on IoT, servers, and connectivity.

Hired from rival Qualcomm late last year, he's an outsider trying to rid Intel of its historical resistance to change. He's also bringing fresh ideas and wholesale changes to  Intel, which promises to bring a new dynamic to the Silicon Valley institution.

IDG News Service spoke with him on a range of topics including VR headsets, IoT, autonomous cars, competitors and the decision to cut products. This is an edited version of the discussion.

IDGNS: How have you settled into your new job? What drew you to Intel?

It's been a really interesting process of acclimation. It's a great mixture of feeling, like an organization where I think my experience and my interests can really help the journey [CEO] Brian [Krzanich] wants to undertake with the company. The scale at which Intel can play is probably going to be very difficult for others to match if you look across, client, networking and the data center groups. The goal is to be able to think as one Intel.

IDGNS: There have been questions on how you would fit into Intel, which has a closed culture and history of promoting executives internally. Many people hired from external companies haven't worked out.

One thing that's really important to understand is that Intel is a company of tremendous heritage. I'm not coming in to fix anything. I'm coming in hopefully to add another dimension and an important ingredient to the management team that Brian has at his disposal. It requires me to respect what Intel has been able to achieve and the caliber of the management team and the brands assimilated. I don't think Brian hired me to maintain the status quo. I think what he wanted was a strong ingredient of outside-in thinking complementing the original thoughts. I'm feeling very comfortable now in being able to feel like I've got a good bunch of colleagues who know where I'm coming from; we can speak straight to each other and we can actually have really good discussions of meritocracy.

IDGNS: You had to make some decisions on cutting products Intel has worked on for years as the company's priorities were reset. How tough was it?

When you come into a company you have a degree of objectivity that isn't tainted by your attachments to the genesis of certain projects. For me it was a fairly structured, objective discussion where you make decisions in a transparent and open manner. As long as you can walk people through your thinking, you can take what was very controversial and make it very logical. I'm passionate about technology but I'm also passionate about profitability and how the two are married in a seamlessly reinforcing way.

IDGNS: What's the reasoning behind cutting mobile processors to focus on modems?

First of all, we rationalized what we were spending our R&D on. We had a couple of mobile SoC products that I don't think were worthy to continue to conclusion. That doesn't mean to say we're no longer doing mobile platforms. On the mobile platform side, my commitment is to talk less and do more. When we have something to say we'll talk about it.

On the modem side, it's a fundamental technology and this is where I think it comes down to being as indelible for us as our competence in CPU or GPU. We've set ourselves up with a very interesting road-map, but more importantly, we've established a degree of credibility, relevance and importance as a key technology partner with a number of key players in the industry that I think is really important.

IDGNS: What are your top priorities and goals?

I have three uber-level goals. One is to continue to drive our client computing business to a position of stable profitability in the face of a slowly declining [market]. I think we're doing well in that area. The second is to grow and scale our IoT business from something that's very interesting to something that's really substantial in the longer term. The IoT business for us is a microcosm of the entire company coming together -- we're creating a type of all-for-one, one-for-all mentality. The third is to maintain a degree of vibrancy in the technology leadership of our entire systems architecture organization. It's developing all the core technologies that really moves the competitive needle forward.

IDGNS: Intel's untethered mixed-reality headset called Project Alloy was big news at IDF. What are the expectations from Alloy and how are things going?

The whole point of having tetherless VR is a big deal. Everything we're doing in Alloy we're going to open-source. We can take VR and evolve it from the very rudimentary definitions today of [VR] in a smart phone that you clip into some kind of visor. You can move it to a capable, embedded PC that's driving two to three teraflops of computing and generate a really immersive experience. That was really it -- taking ideas out from the lab, productizing them, solving all those problems of integration, figuring out how RealSense and depth camera fits into all of that, figuring out how to do merged reality,  and saying "now go scale the ecosystem."

IDGNS: Is the VR headset the new PC?

I think it's another very interesting growth opportunity for the PC. I think it can generate a specific class of products in its own right. It will generate different segmentation points and probably a custom piece of silicon built on the PC platform that amplify the use case. So we're very excited about the whole VR space.

IDGNS: Intel hasn't given up on Moore's Law, though many believe it is reaching its end. How is Intel preparing for a future when manufacturing reaches atomic scale, and how will chips look beyond Kaby Lake?

Nobody inside Intel is coming anywhere near the kind-of-like fatalistic conclusions about where Moore's Law is. Intel has had a stellar track record in delivering node generation like clockwork. Maybe we've moved from a two-year to a two-and-a-half-year cadence, but we already see light at the end of the tunnel. We will continue to drive process technology and nobody is calling timeout on anything. We're working hard on 7-nanometer, we're talking about pathfinding for 5-nanometer. All of that is in the throes. We made a great announcement on Kaby Lake -- that's using an evolution of 14-nanometer transistor geometry that gave a substantially improved user experience compared to Skylake. We're going to continue to do more of that as we continue to drive process leadership.

IDGNS: Are you happy with your current chip line-up -- Kaby Lake for PCs, and Atom for IoT?

We have a competent portfolio of products. I'm in no way shape or form concluding they are complete and aren't going to be benefited from augmentation. For me I think it's really wanting to understand the use cases a lot more. I don't see an IoT strategy for Intel being one where everything is delivered by Intel. It's integrating a number of different technologies that could be indigenous to Intel, or could be created by other companies, but managed in a way where people could look at Intel as somebody providing the overarching framework of integration.

IDGNS: IoT is a big part of Intel's future. What's the strategy for that market?

That's a significant business. I think we're just starting. As you see the advent of autonomous driving vehicles, you see robots and drones start to ship in scale: those are very high value opportunities for us. We characterize our IoT interests into three verticals: industrial, transportation and retail -- all of them have an end-to-end dimension where we're providing a client environment, the networking infrastructure and the data analytics platform that drives all of that through industry partnerships.

IDGNS: Would in any way the ARM foundry deal help Intel achieve its goals in IoT and other areas? Would you be open to the idea of taking an ARM CPU license, as an example?

Open to? Yes. My view is fairly straightforward -- that Intel's IoT plan has to not only be able to harmoniously integrate Intel-based microprocessors and MCUs, it has to be able to aggregate and harmoniously integrate a plethora of different types of MCUs, whether it be ARM-based, MIPS-based, or proprietary MCUs. All of them have the ability to monitor, sense data that they want to get on to an information highway of some kind. Our ability to [support] many different client environments is going to be a necessity in any vertical IoT strategy we have. There are many areas in the ARM ecosystem where Intel can pragmatically play in for its own benefit. I'm a big believer in paying respect to established ecosystems.

IDGNS: Self-driving cars are a big deal for Intel. Could you talk about projects in the pipeline?

Our goal is to provide the type of computing power that dwarfs anything that exists in a car today, but basically make it mainstream. What we're doing on our Xeon Phi processor for machine learning and deep learning, what we're doing in computer vision and also supplemented by radar and lidar. Being able to aggregate that data, generate intelligence, make decisions on it with assistance from machine and deep learning algorithms -- that's all happening as we speak.

IDGNS: How do you see the autonomous car market evolving?

I see the first explosive area to be in the urban transportation environment where  services like Uber and Lyft will evolve and develop. There's going to be a lot of experimentation and path-finding to do in addition to technology creation. We're probably talking about a decade away. Stamina to invest is going to be really important;  those that have the stamina to stay the course are going to win big.

IDGNS: Nvidia is approaching the automotive markets aggressively with its GPUs, how will you compete?

I have a great deal of respect for Nvidia. But every time I think of Nvidia, I think about Californian wine where they can make great wine but it contains only one grape -- great Cabernet Sauvignon or a great Chardonnay. I love French wines and French wines are blends where you need to be great at growing Cabernet, great at growing Merlot, great at growing Cabernet Franc. The art is in the mixture. That's the benefit Intel has. We have GPU, we have CPU, we have custom silicon, we have embedded storage, we have FPGA. Nvidia's going to basically say "I've got GPUs and I've got GPUs and I've got GPUs." Great strategy, but it doesn't give anywhere near the extensibility, flexibility and scalability that Intel is able to offer.

IDGNS: How will 5G influence changes in the way devices are made and work?

5G is as much about the transformation of the network and the infrastructure as it is the client environment. [There is] going to be an even greater demand from mobile broadband bandwidth, people are going to want tens of gigabytes per second, if not hundreds of gigabytes per second. We're going to see much greater pervasiveness of client devices. If you talk about autonomous vehicles or delivering health services over a mobile network, you need to be able to make life or death decisions based on that. The network has to transform and the data center becomes a much higher order entity that's focused on massive data analytics that orchestrates that entire network.

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