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ISS-003 Intel(R) Server Specialist Certification

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ISS-003 exam Dumps Source : Intel(R) Server Specialist Certification

Test Code : ISS-003
Test cognomen : Intel(R) Server Specialist Certification
Vendor cognomen : Intel
: 186 real Questions

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Intel Intel(R) Server Specialist Certification

Intel agency's Skylake Xeon Server Processor Reportedly Packs 32 Cores | killexams.com real Questions and Pass4sure dumps

Microprocessor tremendous Intel (NASDAQ:INTC) recently announced that it had begun shipping a "preliminary creation" version of its next-generation Xeon server processor household. the modern chips are in accordance with the business's Skylake processor structure and are manufactured within the company's 14-nanometer+ technology.

Intel's IvyTown server processor. photo source: Intel. 

It become in the past believed, because of a slip deck that leaked onto the web final yr, that those next-generation processors would pack up to 28 processing cores -- a respectable step up from the 24 institute in the current-era Broadwell-EP-primarily based processor family.

despite the fact, a modern leak -- this time, a purported listing of an engineering sample of the upcoming Skylake Xeon chips on chinese website Taobao -- suggests that the expedient Skylake Server chips may possess 32 cores. (credit goes to AnandTech consumer Sweepr for first spotting this.) 

Let's occupy a better study what this might imply for Intel's statistics-middle business.

Some enterprise historical past

Intel has pointed out during the past that it aims to deliver at least a 20% performance enhancement, SKU-to-SKU, when it introduces a next-generation server processor family unit.

This ability, for example, that after this 12 months's Xeon E5 2698 v4 is replaced by a Xeon E5 2698 v5, the latter might exist at least 20% sooner than the previous. Intel has additionally during the past indicated that it aims to retain pricing roughly flat between generations for a given processor mannequin -- whatever it truly is corroborated by course of the public pricing that Intel publishes for its chips.

So, in effect, efficiency per dollar for Intel's statistics-core chips is anticipated to glide up at a gentle, mighty pace with each and every product cycle. 

besides the fact that children, investors could understand that one of the key increase drivers for Intel's statistics-middle enterprise has been an increase in regular selling prices. That increase comes when statistics-middle processor buyers pick to purchase processors better up in Intel's product stack.

So, for instance, let's account that the ultimate time a company bought Intel-powered servers, it opted for machines powered by using Xeon E5-2660 chips, with a list cost of $1,329. Then, when the server buyer comes back for brand modern methods, it opts for Xeon E5-2680 v4 chips, with a list cost of $1,745.

if that's the case, the server buyer is rarely purchasing from now on chips than it did closing time, however is buying extra high priced processors to better serve its needs. From a income perspective, Intel is rarely selling greater contraptions to this client than it did earlier than, nevertheless it's selling greater computing vigour than it did before -- and it's getting paid for it.

or not it's in Intel's premier pastimes to retain including chips to the suitable of its Xeon processor stack to proceed to motivate upsell to more and more effective and expensive processor models.

About that 32-core Xeon...

With utter that heritage in intellect, they are able to now tolerate in mind why Intel's suggested 32-core Skylake Xeon processor is so pleasing.

The maximum-end Xeon E7 v4 processor became the Xeon E7 8890 v4. This section covered 24 cores, which ran at a foundation clock of 2.2GHz and a highest turbo clock of 3.4GHz. The prior-generation half, the Xeon E7 8890 v3, had 18 cores, which ran at a foundation clock of two.5GHz and a maximum faster hurry of three.3GHz. it truly is 33% more cores at roughly the selfsame speeds, translating into about 33% extra efficiency. 

Had Intel's suitable Skylake Xeon processor protected "just" 28 cores, then the generation-over-generation processor core import number from the revise Xeon E7 v4 to the Xeon E7 v5 would possess long past up just sixteen.7%.

there's additional performance available from the modern Skylake architecture, and Intel may possess adjusted frequencies to do up for the smaller core import number raise, but the latter may additionally possess had a negative possess an result on on power effectivity.

by using bringing out a 32-core model on the very desirable of its subsequent-generation Xeon processor stack, Intel ensures that it may possibly carry at the least the selfsame ilk of efficiency increase at the very perquisite of its Xeon E7 v5 stack because it did with the Xeon E7 v4 chips. 

Intel's statistics-middle business is anticipated to grow by lower than 10% this year. Given the fresh slowdown in its growth cost, Intel cannot approach up with the money for to let up on the performance advancements that it brings to the table.  


Intel certification goes bust | killexams.com real Questions and Pass4sure dumps

considering Intel certification? don't—unless an IT manager is requiring you to earn Intel accreditation. The program's on its final legs. glean the particulars in this week's IT Certification corner.

conclude out of the funds in a stakes race, and you’ll exist labeled an additionally-ran. With Microsoft, Cisco, and CompTIA grabbing most certification headlines, is there scope in the highlight for Intel accreditation? curiously no longer. however more on that in a moment. Don’t leave out an installment!get hold of Erik Eckel’s IT Certification corner for your electronic mail container each Friday and trap every column, along with timely assistance and reviews not institute on the website! It’s handy, and it’s free. simply retreat to the TechMails page and register for IT Certification nook to ensure that you retain up up to now on the newest certification information, shortcuts, information, and more! I account there’s scope for four or five top-shelf IT trade certification classes. which of them? are trying these:
  • Cisco
  • CompTIA
  • Microsoft
  • Novell
  • pink Hat
  • Intel doesn’t do my listing. The hardware company boasts dominant participate in many peripheral markets, though, so I determined to notice into its certification software.

    Intel certification respiratory closing gaspIf you should receive Intel certification, time is operating out. Intel simply announced that it could exist transferring utter “licensed people into trade necessities based certification courses.” That competence Intel is shutting down its certification programs.

    as an alternative of working its personal proprietary effort, Intel may exist pushing IT experts into other diagnosed classes. It has chosen ProsoftTraining and CompTIA to substitute its certifications.

    however, Intel will deference utter Intel certifications in the course of the conclusion of the yr. it's going to additionally proceed proposing technical working towards for its products, including practicing for Intel Premier providers. but approach Dec. 31, 2001, that’s it for Intel’s three certification tracks.

    The tracksIntel’s three certification tracks are:

  • Intel certified e-business specialist
  • Intel licensed Integration professional
  • Intel certified options advisor
  • The e-enterprise professional tune specializes in checking out e-business deployment and integration capabilities, while the integration professional route offers candidates two tracks: networking and server integration. The options consultant tune displays an independent IT professional’s skill to enhance and enforce strategic technical solutions.

    right here’s a quick notice at each and every of Intel’s certifications.

    Intel certified e-business SpecialistAccording to Intel, the e-company track is suggested for IT specialists “chargeable for designing and optimizing e-company solutions in line with Intel’s broad product line.” covered in that community are internet consultants and programs integrators.

    E-biz cert candidates should further skills with Intel’s NetStructure e-enterprise records middle methods, 1U/2U/4U server structures, and different e-business product lines the enterprise manufactures.

    predict to occupy classes in here in case you’re drawn to an Intel licensed e-enterprise professional:

  • E-commerce methods and infrastructure design
  • Server foundations
  • WAN solutions
  • protection administration
  • Intel certified Integration SpecialistAs i discussed, the combination specialist certification offers two tracks: the community route and the server concentration. These tracks goal IT group of workers “answerable for technical activities,” which, of course, describes very nearly each IT professional obtainable.

    Intel’s breakdown is as follows:

  • Networking—methods engineers, network directors, techniques builders, and other technical IT personnel
  • Server Integration—server and techniques integrators
  • The networking song emphasizes learning key technologies vital to design and set up cozy, productive networks. Core courses encompass getting to know Intel expertise necessities and imposing LAN and WAN solutions. Networking electives encompass courses overlaying:
  • instant LAN design.
  • E-business infrastructure optimization.
  • protection and VPN administration.
  • Scalable community design and administration.
  • The server integration music examines actual server creation. Candidates could exist verified on their capability to design, build, examine, and upgrade servers. I smile a runt bit after I read Intel’s commentary that this music takes a multivendor method. Why conclude I feel Pentium processor knowledge is emphasised over AMD Athlon chip talents?

    The server integration song’s core necessities consist of Intel Server applied sciences and Configuration and Managing and Servicing Intel-based Servers. (but don’t forget, a multiplatform strategy is used!) In Intel’s protection, a candidate possess to additionally possess CompTIA’s Server+ certification. CompTIA’s accreditation, of course, makes consume of a multivendor strategy.

    apparently, no elective classes are required to earn Intel’s server integration certification.

    Intel licensed solutions ConsultantIntel’s third certification goals excessive-stage systems engineers and network directors. however it too has relevance for IT managers, consultants, and different IT authorities. Intel states the Intel certified options advisor certification is supposititious for these IT specialists “whose basic responsibilities are the analysis, suggestion, and design of advanced expertise solutions.”

    Core necessities that must exist mastered encompass e-commerce design and server and network fundamentals. As with the Intel certified Integration professional Server Integration tune, no electives are required.

    Is there one other certification you’d want to examine?in case you’re inquisitive about other IT industry certifications, let me comprehend. whereas it can not exist possible to reply utter requests, I may exist in a position to verify the certification you possess got questions on! Six steps to successThe Intel certification application includes six steps:
  • attain a consumer id from Intel.
  • switch respective credits, if you’ve already earned MCSE, CCNA, CNE, CIW, or CompTIA certification.
  • choose core requirements.
  • choose non-compulsory classes.
  • determine a practicing facility and acquire training.
  • move the essential tests.
  • As with different certifications, candidates that meet the crucial requirements relish access to an Intel publication, an exclusive web site, an tips archive, and greater (at the least unless the finish of the 12 months).

    Intel’s greatest problemHave you ever heard of Intel’s certification program? possess you ever met a licensed Intel IT expert? apart from a stray advertisement for Intel’s certification software, I’d certainly not heard of it.

    I decided to retreat looking and spot how tons I might find. Neither my autochthonous working towards facility nor modern Horizons offered any classes I may locate concentrated on Intel certification. Worse, I couldn’t find any assistance on Intel’s application at Brainbench, GoCertify, or Cramsession. Certification journal listed Intel certification as a topic, but I couldn’t find any distinctive counsel there, either.

    devoid of such third-birthday party assist and as a minimum some publicity, it’s difficult for a certification to occupy off. consequently, it’s no shock to me that Intel is shutting the doorways on its proprietary certification program.

    Eckel’s takeI can’t, in expedient faith, recommend that you pursue Intel certification. It’s ending in lower than a yr. Worse, no one i conclude know ever knew a grand deal about it.

    however you earned the accreditation, what conclude you feel the percentages are that an IT supervisor or CIO (the individuals you necessity to stir with the cert) has heard of Intel’s program? here’s a hint: the chances are more desirable that i'll win the Kentucky Derby. and i don’t personal a horse—simply a few canine.

    have you ever heard of Intel certification?We notice forward to getting your enter and listening to your experiences involving this subject. exist a section of the discussion below or ship the editor an electronic mail.  

    Intel Tells TheStreet how it's Going After Nvidia within the A.I. Chip Market | killexams.com real Questions and Pass4sure dumps

    As Intel (INTC) tries to gain participate in opposition t Nvidia (NVDA) in a quick-starting to exist server AI chip market, the enterprise is desperate to cowl essentially every viable base.

    I recently had an chance to consult with Intel's San Diego offices, the station -- following the business's 2016 acquisition of San Diego-based AI chip startup Nervana programs -- the business does an terrible lot of its AI-connected silicon and software R&D work. at the assembly possess been Arjun Bansal, Intel's VP of AI utility and analysis, and Casimir Wierzynski, a Senior Director for Intel's AI research neighborhood.

    where the Server AI Chip Market Market Stands today

    The marketplace for chips used to deal with AI/deep getting to know workloads inside information facilities can exist crash up into two businesses:

  • Chips used through powerful computing methods that educate AI fashions to conclude issues comparable to possess in mind voice instructions, observe objects inside pictures or aid a vehicle power itself around metropolis streets.
  • Chips that race knowledgeable AI models towards modern facts and content material -- as an example, a request with the aid of a mobile app to uphold procedure a voice command or bring personalised word feed content material. This endeavor, referred to as inference, is an terrible lot much less computationally traumatic than practising and can exist dealt with each by means of servers and by means of conclusion-person hardware equivalent to telephones, PCs and automobiles.
  • although competition is birth to resolve upon up a bit, a really grand percentage of AI practising work remains handled by means of Nvidia's Tesla server GPU family unit. loads of server inference work, by using evaluation, has traditionally been dealt with by means of Intel's Xeon server CPUs. youngsters, a growing component of inference work is now being handled by course of accelerators such as Nvidia GPUs, programmable chips (FPGAs) from Intel and Xilinx  (XLNX) and customized-designed chips (ASICs) comparable to Alphabet/Google's (GOOGL) Tensor Processing gadgets (TPUs - they can too exist used for practising) and Amazon.com's (AMZN) modern AWS Inferentia chip.

    Intel's Server AI Chip strategy

    Whereas Nvidia's server AI chip efforts revolve absolutely round GPUs featuring really expedient processing cores for AI workloads (they may exist referred to as Tensor Cores), Intel plans to uphold a grand selection of chip architectures. Its existing and deliberate server AI choices include:

  • The NNP-L1000 and NNP-I, a pair of Nervana ASICs which are respectively meant for working towards and inference. each are promised to enter construction later this yr. fb (FB) has been a construction associate for Intel's AI ASICs.
  • FPGAs that can too exist used for inference. Microsoft (MSFT)  and Baidu  (BIDU) consume Intel's FPGAs for AI work.
  • A server GPU lineup. The enterprise's first server GPU(s) are anticipated in 2020.
  • DL increase, a collection of technologies supposititious to ameliorate the inference efficiency of Xeon CPUs. the primary version of DL boost was brought with Intel's lately-unveiled Cascade Lake Xeon CPUs.
  • When requested about the competitive strengths of the NNP-L1000 relative to offerings similar to Nvidia's Tesla GPUs, Bansal changed into wanting to factor out that the chip has been designed from the ground as much as instruct AI/deep studying models and for this judgement would not possess to subject itself with pictures-related services. "We conclude not necessity to spend any die zone on pics-linked compute," he observed.

    He additionally pointed out that due to the NNP-L1000's unique processing architecture (it relies on a bunch encoding layout known as bfloat16), the chip can consume sixteen-bit multiplier circuits to deliver efficiency akin to what GPUs require 32-bit multipliers for. This results in smaller and extra power-effective multipliers, and (given that the multipliers require half as a lot data) doubles the chip's valuable reminiscence bandwidth.

    alongside equivalent lines, Bansal argued that within the inference area, the NNP-I should exist very aggressive relative to FPGAs "from a power-performance point of view," and convey effective performance for workloads akin to computing device translation, speech attention and counsel systems. at the equal time, he referred to that some clients will nonetheless resolve upon FPGAs due to their capacity to reconfigured to exploit modern tasks.

    When requested about how Intel sees server CPUs getting used for inference as exact for accelerators takes off, he advised that corporations will nevertheless consume idle CPU means for inference work. "individuals possess a lot of dormant [server] means now and then," he noted.

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    The magnitude of software

    in addition to its gigantic chip R&D investments, Nvidia's dominant station within the AI training silicon market has a lot to conclude with the developer ecosystem it has constructed out. This ecosystem is underpinned through the enterprise's CUDA programming mannequin and related CUDA profound Neural community (cuDNN) application library, which supports probably the most accustomed profound getting to know utility frameworks (and some much less-general ones).

    although it has created profound getting to know utility libraries that are optimized for its CPUs, Intel's routine for chipping away at Nvidia's grand developer mindshare would not revolve around creating a direct emulate to CUDA and cuDNN, however on riding adoption of an reply called nGraph. nGraph is a compiler -- a software that translates code from a programming language into computer code that may too exist completed by using a processor -- intended to work with lots of profound gaining knowledge of frameworks across a lot of processor types (Xeon CPUs, Nervana ASICs and even Nvidia GPUs) for both practising and inference work.

    Intel argues that considering that many AI software frameworks had been optimized for a particular contour of processor (in many instances, Nvidia's GPUs), or not it's regularly too intricate these days to port an AI mannequin counting on one ilk of processor to one other category of processor, and that it may well too exist too difficult to glean a mannequin to race on a different framework. nGraph, the company insists, does away with such challenges.

    whereas hand-optimized AI utility libraries can too exist advantageous when an organization is counting on just one processing structure, they are not when a corporation is the consume of three or four of them. "And there are advantages for having three or 4 in its station of getting one," Bansal asserted.

    The problem for Intel, of course, is convincing companies and cloud giants that they should quiet exist the usage of more than one architecture at a time when many are completely relying on Nvidia's GPUs for AI working towards. If an organization chooses to preserve totally counting on Nvidia's GPUs for practicing, it is going to doubtless persist with Nvidia's widely-supported application tools. on the other hand, if the AI working towards silicon market begins to fragment, Intel's revenue pitch for nGraph turns into a lot superior.

    one after the other, Wierzynski brought up that Intel is additionally investing in software options to exploit AI privacy concerns, equivalent to an open-supply reply for the processing of encrypted AI facts. One consume case he gave for the answer: A medical institution may ship encrypted facts to a radiologist working remotely without a affected person data being shared, and the radiologist might ship again an encrypted edition of his or her reply to the health center.

    The great photograph

    it exist not going that Nvidia will capitulation its latest lead within the AI training processor market every time soon, primarily when you account that it's additionally investing closely in the space. And whereas the server inference processor market is extra aggressive, Intel might approach to exist being one among a few bold avid gamers there, together with the likes of Nvidia and Xilinx.

    although, Intel does possess a unique silicon and utility routine for starting to exist its AI accelerator income, and is certainly putting its funds where its mouth is. And even though a lot continues to exist to exist shared about their accurate performance and vigour consumption, the indisputable fact that the enterprise's Nervana ASICs are being built from the floor up to tackle AI work could assist them achieve some success.

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    Build AI that works offline with Coral Dev Board, Edge TPU, and TensorFlow Lite | killexams.com real questions and Pass4sure dumps

    Posted by Daniel Situnayake (@dansitu), Developer Advocate for TensorFlow Lite.

    When you assume about the hardware that powers machine learning, you might picture endless rows of power-hungry processors crunching terabytes of data in a far-off server farm, or hefty desktop computers stuffed with banks of GPUs.

    You’re unlikely to picture an integrated circuit, 40mm x 48mm, that sits neatly on a development board the size of a credit card. You definitely won’t picture a slick aluminium box, the size of a toy car, hooked to your computer with a separate USB-C.

    These modern devices are made by Coral, Google’s modern platform for enabling embedded developers to build fabulous experiences with local AI. Coral’s first products are powered by Google’s Edge TPU chip, and are purpose-built to race TensorFlow Lite, TensorFlow’s lightweight solution for mobile and embedded devices. As a developer, you can consume Coral devices to explore and prototype modern applications for on-device machine learning inference.

    Coral’s Dev Board is a single-board Linux computer with a removable System-On-Module (SOM) hosting the Edge TPU. It allows you to prototype applications and then scale to production by including the SOM in your own devices. The Coral USB Accelerator is a USB accessory that brings an Edge TPU to any compatible Linux computer. It’s designed to fitting perquisite on top of a Raspberry Pi Zero.

    In this blog post, we’ll exist exploring the exciting applications of machine learning “at the edge”, and we’ll learn how TensorFlow Lite and Coral can uphold you build AI into hardware products. You’ll notice how their pre-trained models can glean you up and running with no ML experience, and meet the conversion tools that uphold you optimize your own models for local use.

    Inference at the edge

    Until recently, deploying a machine learning model in production meant running it on some benign of server. If you wanted to detect known objects in a video, you’d possess to stream it to your backend, where some powerful hardware would race inference (i.e. detect any objects present) and inform the device of the results.

    This works just fine for many applications, but what if they could conclude without the server and wait on-device?

    TensorFlow Lite makes it possible to race high performance inference at the edge, which enables some fabulous modern experiences.

    Offline inference

    With edge inference, you no longer reliance on an Internet connection. If internet is unhurried or expensive, you can build smart sensors that understand what they notice and only transmit the titillating parts.

    This means you can deploy ML in totally modern situations and places. Imagine a smart camera that identifies wildlife in remote locations, or an imaging device that helps do medical diagnoses in areas far from wireless infrastructure.

    Monitoring wildlife with smart cameras Minimal latency

    Sending data to a server involves a round-trip delay, which gets in the course when working with real-time data. This is no longer an issue when their model is at the edge!

    When inference is super speedy they can solve problems that depend on performance, fancy adding real-time remonstrate tracking to a robot that navigates human spaces, or synthesizing audio during a live musical performance.

    Synthesizing audio during a live performance Privacy and security

    When data stays at the edge, users benefit from increased privacy and security, since personal information never leaves their devices.

    This enables new, privacy-conscious applications, fancy security cameras that only record video when a likely threat is detected, or health devices that resolve personal metrics without sending data to the cloud.

    Analyzing health metrics without sending data to the cloud Performance

    All of these modern applications depend on high-performance inference that is only possible with hardware acceleration by the Edge TPU. But how much does this really hurry things up?

    In their internal benchmarks using different versions of MobileNet, a robust model architecture commonly used for image classification on edge devices, inference with Edge TPU is 70 to 100 times faster than on CPU.

    Using MobileNets for mug detection we’ve race inference at up to 70 to 100 frames per second, and for food recognition we’ve achieved 200 frames per second. This is enough for better-than-real-time inference of video, even when running several models simultaneously.

    The following table outlines performance of the Coral USB Accelerator on various models.

    * Desktop CPU: 64-bit Intel(R) Xeon(R) E5–1650 v4 @ 3.60GHz

    ** Embedded CPU: Quad-core Cortex-A53 @ 1.5GHz

    † Dev Board: Quad-core Cortex-A53 @ 1.5GHz + Edge TPU

    All tested models were trained using the ImageNet dataset with 1,000 classes and an input size of 224x224, except for Inception v4 which has an input size of 299x299.

    Get started with Coral and TensorFlow Lite

    Coral is a modern platform, but it’s designed to work seamlessly with TensorFlow. To bring TensorFlow models to Coral you can consume TensorFlow Lite, a toolkit for running machine learning inference on edge devices including the Edge TPU, mobile phones, and microcontrollers.

    Pre-trained models

    The Coral website provides pre-trained TensorFlow Lite models that possess been optimized to consume with Coral hardware. If you’re just getting started, you can just download a model, deploy it to your device, and instantly commence running image classification or remonstrate detection for various classes of objects using Coral’s API demo scripts.

    Retrain a model

    You can customize Coral’s pre-trained machine learning models to recognize your own images and objects, using a process called transfer learning. To conclude this, follow the instructions in Retrain an existing model.

    Build your own TensorFlow model

    If you possess an existing TensorFlow model, or you’d fancy to train one from scratch, you can follow the steps in Build a modern model for the Edge TPU. Note that your model must meet the Model requirements.

    To prepare your model for the Edge TPU, you’ll first transfigure and optimize it for edge devices with the TensorFlow Lite Converter. You’ll then compile the model for the Edge TPU with the Edge TPU Model Compiler.

    Quantization and optimization

    The Edge TPU chips that power Coral hardware are designed to work with models that possess been quantized, import their underlying data has been compressed in a course that results in a smaller, faster model with minimal repercussion on accuracy.

    The TensorFlow Lite Converter can execute quantization on any trained TensorFlow model. You can read more about this technique in Post-training quantization.

    The following code snippet shows how simple it is to transfigure and quantize a model using TensorFlow Lite nightly and TensorFlow 2.0 alpha:

    Next steps

    We’ve heard how you can consume Coral and TensorFlow Lite to build brand modern experiences that bring AI to the edge. If you’re looking to glean started, here’s how:

    Feeling inspired? We’re excited to notice what their community builds with Coral and TensorFlow Lite! participate your ideas on Twitter with the hashtags #withcoral and #poweredbyTF.


    New progression of Artesyn CRPS Server Power Supplies Includes Industry's Highest Power 2.4 kW Model | killexams.com real questions and Pass4sure dumps

    HONG KONG, 2019年3月18日 - (Media OutReach) - At the Open Compute acme today, Artesyn Embedded Technologies announced a modern progression of power supplies following the Intel(R) Common Redundant Power Supply (CRPS) specification, which includes a 2400 watt model, the highest power available in the industry in this contour factor, offering a market-leading power density of 75 watts per cubic inch (W/in3).

    With individual power ratings from 550 W up to 2400 W, Artesyn's modern CSU progression provides makers of compute, networking and storage infrastructure in enterprise IT, cloud and hyperscale environments with power capacity flexibility. The common form, fit, and function for utter products in the progression future-proofs system designs. The progression can address a server manufacturer's compass from cost-sensitive entry plane systems to power-hungry, high performance servers with space constraints. The CSU progression is housed in a compact 1U high enclosure measuring just 2.89 x 7.28 inches (73.5 x 185.0 mm), achieved by using the latest power switching technology and high density component packaging techniques.

    The CSU progression generates a main output of 12.2 Vdc for feeding downstream dc-dc converters in systems using distributed power architectures. A 12 Vdc standby output can exist used for power management or supervisory circuitry. Many of the models in Artesyn's CSU progression are available with options for input and airflow direction, enabling deployment in environments from enterprises to traditional data centers, -48 Vdc data centers, and telecom central offices. The DC input option can too exist used to power outfit from battery backup.

    Active current sharing helps maximize cost effectiveness by eliminating the necessity for additional components when paralleling multiple power supplies for very high current applications. These hot-pluggable power supplies uphold N+1 or N+N redundant architectures, frosty redundancy mode, and system power throttling.

    All AC-input models in the family are certified for 80 PLUS(R) Platinum plane efficiency, peaking at 94%, and present low total harmonic current distortion (iTHD) at light loads (EN61000-3-2).

    CSU progression power supplies can exist remotely set-up, monitored and controlled using Artesyn's universal PMBus(TM) graphical user interface, which enables users to implement sophisticated power management schemes with minimal additional components.

    All models present overcurrent, overvoltage, undervoltage, overtemperature and fan fault protection.

    Artesyn will exist showcasing the CSU progression and other power technology related to the Open Compute Project (OCP) on booth D7 at the San Jose Convention headquarters on March 14th and 15th.

    Media Contact:Alice Hui+852 2176 3548[email protected]

    トピック: Press release summaryセクター: Electronicshttp://www.acnnewswire.comFrom the Asia Corporate word Network

    Copyright © 2019 ACN Newswire. utter rights reserved. A division of Asia Corporate word Network.


    Intel Tells TheStreet How It's Going After Nvidia in the A.I. Chip Market | killexams.com real questions and Pass4sure dumps

    As Intel (INTC) tries to gain participate against Nvidia (NVDA) in a fast-growing server AI chip market, the company is enthusiastic to cover almost every possible base.

    I recently had a desultory to visit Intel's San Diego offices, where -- following the company's 2016 acquisition of San Diego-based AI chip startup Nervana Systems -- the company does much of its AI-related silicon and software R&D work. At the meeting were Arjun Bansal, Intel's VP of AI Software and Research, and Casimir Wierzynski, a Senior Director for Intel's AI Research group.

    Where the Server AI Chip Market Market Stands Today

    The market for chips used to exploit AI/deep learning workloads within data centers can exist split into two groups:

  • Chips used by powerful computing systems that train AI models to conclude things such as understand voice commands, detect objects within photos or uphold a car drive itself around city streets.
  • Chips that race trained AI models against modern data and content -- for example, a request by a mobile app to uphold process a voice command or deliver personalized word feed content. This activity, known as inference, is much less computationally demanding than training and can exist handled both by servers and by end-user hardware such as phones, PCs and cars.
  • Though competition is starting to pick up a bit, a very great percentage of AI training work is quiet handled by Nvidia's Tesla server GPU family. A lot of server inference work, by comparison, has historically been handled by Intel's Xeon server CPUs. However, a growing portion of inference work is now being handled by accelerators such as Nvidia GPUs, programmable chips (FPGAs) from Intel and Xilinx  (XLNX) and custom-designed chips (ASICs) such as Alphabet/Google's (GOOGL) Tensor Processing Units (TPUs - they can too exist used for training) and Amazon.com's (AMZN) modern AWS Inferentia chip.

    Intel's Server AI Chip Strategy

    Whereas Nvidia's server AI chip efforts revolve completely around GPUs featuring specialized processing cores for AI workloads (they're known as Tensor Cores), Intel plans to uphold a wide array of chip architectures. Its current and planned server AI offerings include:

  • The NNP-L1000 and NNP-I, a pair of Nervana ASICs that are respectively meant for training and inference. Both are promised to enter production later this year. Facebook (FB) has been a development partner for Intel's AI ASICs.
  • FPGAs that can exist used for inference. Microsoft (MSFT)  and Baidu  (BIDU) consume Intel's FPGAs for AI work.
  • A server GPU lineup. The company's first server GPU(s) are expected in 2020.
  • DL Boost, a set of technologies meant to ameliorate the inference performance of Xeon CPUs. The first version of DL Boost was introduced with Intel's recently-unveiled Cascade Lake Xeon CPUs.
  • When asked about the competitive strengths of the NNP-L1000 relative to offerings such as Nvidia's Tesla GPUs, Bansal was enthusiastic to point out that the chip has been designed from the ground up to train AI/deep learning models and thus doesn't possess to concern itself with graphics-related functions. "We don't possess to spend any die zone on graphics-related compute," he said.

    He too pointed out that thanks to the NNP-L1000's unique processing architecture (it relies on a number encoding format known as bfloat16), the chip can consume 16-bit multiplier circuits to deliver performance comparable to what GPUs require 32-bit multipliers for. This results in smaller and more power-efficient multipliers, and (since the multipliers require half as much data) doubles the chip's effective recollection bandwidth.

    Along similar lines, Bansal argued that in the inference space, the NNP-I will exist very competitive relative to FPGAs "from a power-performance perspective," and deliver stout performance for workloads such as machine translation, speech recognition and recommendation systems. At the selfsame time, he famed that some customers will quiet prefer FPGAs due to their competence to reconfigured to exploit modern tasks.

    When asked about how Intel sees server CPUs being used for inference as exact for accelerators takes off, he suggested that companies will quiet consume idle CPU capacity for inference work. "People possess a lot of dormant [server] capacity at times," he noted.

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    The consequence of Software

    In addition to its great chip R&D investments, Nvidia's dominant position in the AI training silicon market has much to conclude with the developer ecosystem it has built out. This ecosystem is underpinned by the company's CUDA programming model and related CUDA profound Neural Network (cuDNN) software library, which supports the most current profound learning software frameworks (and some less-popular ones).

    Though it has created profound learning software libraries that are optimized for its CPUs, Intel's strategy for chipping away at Nvidia's massive developer mindshare doesn't revolve around creating a direct emulate to CUDA and cuDNN, but on driving adoption of a solution known as nGraph. nGraph is a compiler -- a program that translates code from a programming language into machine code that can exist executed by a processor -- meant to work with a variety of profound learning frameworks across a variety of processor types (Xeon CPUs, Nervana ASICs and even Nvidia GPUs) for both training and inference work.

    Intel argues that since many AI software frameworks possess been optimized for a particular benign of processor (in many cases, Nvidia's GPUs), it's often too difficult today to port an AI model relying on one ilk of processor to another ilk of processor, and that it can too exist too hard to glean a model to race on a different framework. nGraph, the company insists, does away with such challenges.

    While hand-optimized AI software libraries can exist effective when a company is relying on just one processing architecture, they aren't when a company is using three or four of them. "And there are advantages for having three or four instead of having one," Bansal asserted.

    The challenge for Intel, of course, is convincing enterprises and cloud giants that they should exist using more than one architecture at a time when many are exclusively relying on Nvidia's GPUs for AI training. If a company chooses to retain solely relying on Nvidia's GPUs for training, it will likely stick with Nvidia's widely-supported software tools. On the other hand, if the AI training silicon market starts to fragment, Intel's sales pitch for nGraph becomes a lot stronger.

    Separately, Wierzynski pointed out that Intel is too investing in software solutions to address AI privacy concerns, such as an open-source solution for the processing of encrypted AI data. One consume case he gave for the solution: A hospital could ship encrypted data to a radiologist working remotely with no patient info being shared, and the radiologist could ship back an encrypted version of his or her reply to the hospital.

    The grand Picture

    It's unlikely that Nvidia will capitulation its current lead in the AI training processor market anytime soon, particularly given that it's too investing heavily in the space. And while the server inference processor market is more competitive, Intel could finish up being one of several formidable players there, along with the likes of Nvidia and Xilinx.

    However, Intel does possess a unique silicon and software strategy for growing its AI accelerator sales, and is clearly putting its money where its mouth is. And though much remains to exist shared about their exact performance and power consumption, the fact that the company's Nervana ASICs are being built from the ground up to exploit AI work could uphold them achieve some success.

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