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920-533 - Carrier VoIP - Communication Server 2000 - Compact - Dump Information

Vendor : Nortel
Exam Code : 920-533
Exam Name : Carrier VoIP - Communication Server 2000 - Compact
Questions and Answers : 33 Q & A
Updated On : October 15, 2018
PDF Download Mirror : [920-533 Download Mirror]
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920-533 Carrier VoIP - Communication Server 2000 - Compact

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920-533 exam Dumps Source : Carrier VoIP - Communication Server 2000 - Compact

Test Code : 920-533
Test Name : Carrier VoIP - Communication Server 2000 - Compact
Vendor Name : Nortel
Q&A : 33 Real Questions

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Senior Telecom industry Leaders be part of Tethr Board to drive subsequent Wave of Ai-enabled Communications Innovation | killexams.com Real Questions and Pass4sure dumps

No outcomes found, are trying new keyword!These additions to Tethr come at a time when the business is experiencing huge increase within the telecommunications trade by means of direct relationships with carriers and primary BPOs ... on the boards of ...

Morning Market Pulse - China performs "capture-down" after Golden Week | killexams.com Real Questions and Pass4sure dumps

Mike van Dulken, Head of research at Accendo Markets, commented to customers this morning:

FTSE 100 Index known as to open flat at 7318, but returned under the 7323 key stage (mid-Sept breakout) it re-demonstrated late Friday. Bulls need a spoil above 7340 overnight highs. Bears require a meaningful breach of the 7300 lows demonstrated Friday. Watch stages: Bullish 7340, Bearish 7300

 

calls for a muted open come after a downbeat birth to the week in Asia. Most markets are lessen but China is suffering most (steepest one-day losses of 2018) because it returns from the Golden week break, reacting to alternate concerns (US not enabling Mexico or Canada strike exchange deals with China), better bond yields and allegations of secret agent chips implanted by way of semiconductor manufacturers.

 

China’s PBOC valuable bank moved to restrict declines by lowering Banks’ Reserve Requirement Ratios (RRR), easily loosening economic policy via $109bn. potent China PMI services additionally did not support however might yet raise FTSE Miners, however we be aware twin-listed (London-Sydney) sector contributors down up to 3% in Australia overnight.

 

In company information this morning responds to media hypothesis and confirms discussions with Lloyds Banking a couple of wealth management alliance. Fox offer for Sky lapses. Struggling UK retailer confirms speculation it may be up on the market.

 

Unite Q3 portfolio valuation; USAF scholar lodging portfolio £2.23bn, +1.1% QoQ like-for-like, 5.three% yield, ninety eight% of beds let; 9 LSAV investment portfolio £1.2bn, +1.6%, four.6% yield. Lancashire Holdings divulges $30m publicity to loss movements inside marine portfolio and accrued attritional losses of $25-45m from natural catastrophes. Expects terrible Q3 return on equity

 

completes £2.4bn buyout of Nortel Networks UK pension plan (over 22K participants) bringing its complete extent for pension de-risking business to £8.4bn YTD. makes £4.4m bolt-on acquisition; Caledonian Holdings; 2.4x earnings. IP community announced failure of a congenital adrenal hyperplasia (CAH) phase III scientific drug trial in one of its portfolio organizations (Diurnal).

 

XP power 9M revenues +18% (+24% at regular FX, +eleven% like-for-like); orders +eleven% YoY (+18% at constant FX; +eight% like-for-like). trading been robust; Upped inventories to resolve considerations. Div +6%. Expects results in-line with consensus. Mitie neighborhood acquires imaginative and prescient protection (£192.1m income, £2.7m pre-tax loss in 2017) from for £14m (debt free), adding £forty two.4m in belongings.

 

reach Q3 trading profits +21% YoY, reflecting acquisition of & big name. Like-for-like income -7%. Circulation ad income -4%, Publishing -20%, Digital +7%, Transactional +12%. on course to bring £2m integration discounts from categorical & big name M&A in 2018 and as a minimum £20m p.a. by way of 2020.

 

In focal point for this quite quiet week (specifically today; US Columbus Day holiday) will be a batch of UK macro data (all Weds). This includes August GDP (Weds, 9:30am) expected +0.1% mom (vs +0.3% in July). That noted, the three-month commonplace growth continues to be projected at an unchanged 0.6%.

 

in the meantime the uk change Deficit (Weds, 9:30am) is viewed widening to £1.8bn in August, reversing many of the June-July improvements to the smallest deficit on the grounds that February. Watch GBP pairs for reaction, with a knock-on for FTSE.

 

In different UK macro statistics, Industrial production (Weds, 9:30am) is expected to have accelerated in August (est. 1% YoY vs. 0.9% prev.), while Manufacturing production holds company at 1.1% YoY. In a more being concerned signal for FTSE Housebuilders, building Output forecast to gradual to 1.4% YoY increase after a 3.5% growth in July that extended April’s accelerating rebound.

 

The leisure of the week is gentle on statistics releases, apart from US Inflation (Thurs, 1:30pm). Headline is projected to tug returned in September (est. 2.four% YoY towards 2.7% prev.), but the greater sturdy core metric enterprise at 2.three% YoY (2.2% in August). signs of economic overheating may add yet greater fears of the Fed needing to be more hawkish.


Intangible assets power boom, Acquisitions in the prescription drugs Sector | killexams.com Real Questions and Pass4sure dumps

manhattan, October 4, 2018 /PRNewswire/ --

CannabisNewsWire Editorial coverage

Intangible assets equivalent to patents more and more dominate the international company landscape, principally in the pharmaceutical sector.

  • Intangible assets are estimated to be value trillions of dollars within the united states by myself.
  • These property pressure offers as businesses purchase smaller patent holders.
  • In growing sectors similar to hashish, smaller companies are speeding to set up intangible property competent for anticipated market boom.
  • Lexaria Bioscience Corp. (CSE:LXX) (OTC:LXRP) (LXRP Profile) is likely one of the leaders on this planet of cannabis, with functions made for greater than 50 distinct patents and greater to come. Tilray Inc. (NASDAQ:TLRY) is also specializing in building its highbrow property (IP) portfolio together with unique rights to at least 22 issued or pending patents. In wake of receiving FDA approval of its Epidiolex (cannabidiol) oral answer for the remedy of seizures linked to two infrequent and severe sorts of epilepsy, GW pharmaceuticals Plc (NASDAQ:GWPH) has utilized for 5 new Epidiolex patents. currently canopy increase agency (NYSE:CGC) introduced that it has, on my own or with its subsidiary or joint-undertaking companions, filed eight provisional U.S. patents concerning the beginning and utility of cannabis and cannabinoid-primarily based therapeutics. and thru its possession of CanniMed, Aurora cannabis, Inc. (OTC:ACBFF) has received six patents regarding cannabinoid delivery for ache administration.

    To view an infographic of this editorial, click on right here.

    The vigor of highbrow Property

    intellectual property is now probably the most crucial parts of the global financial system. Copyrights, trademarks and patents are a vital weapon within the arsenal of many businesses. For some agencies, here is a secondary consideration, a means to enhance and offer protection to their positions within their fields. For others, it's their total center of attention - a way to dominate a sector or supply effective returns.

    The vigor of IP ability that no a part of the economic climate goes untouched. New industries frequently arise off the returned of new IP. Even when it really is no longer the case, the development of recent thoughts and applied sciences generally result in the advent of patents, as companies work to innovate and manage suggestions that supply them an part over their opponents. For surprisingly new sectors, such as the cannabis market, this potential the sudden flourishing of IP and a race by using corporations to stake their declare on these beneficial belongings. As a sector matures, so will its IP market.

    The price of Intangible property

    Patents are a large part of company for corporations reminiscent of Lexaria Bioscience Corp. (CSE:LXX) (OTCQX:LXRP), for whom developing and protecting new technology is basic to success. no matter if or not it's underlying expertise such as DehydraTECH, the company's equipment for increasing the body's absorption of chemical substances, or particular items derived from DehydraTECH, comparable to TurboCBD, Lexaria's success is based on its diverse IP.

    IP is a part of a much broader sector of the economic system valued at trillions of bucks within the united states by myself - intangible property. here is the wealth provided not via physical belongings and the tactics that without delay radically change them however by using less visible, more summary resources. It includes IP, whose cost is commonly examined and measured, in addition to more durable-to-pin-down facets akin to business methods and attractiveness.

    These components have always performed a part in business, however their becoming awareness and evaluation has resulted in the increase of groups whose leading center of attention is on intangible belongings, from funding research corporations and reinsurers to patent licensing and enforcement groups.

    Representing round a third of the U.S. financial system, intangible assets at the moment are consciously analyzed and developed through pretty much every company, although they don't appreciate the "intangible asset" label. groups with a high-quality focus on these areas can place themselves in positions of tremendous energy.

    Patents in a new Sector

    The velocity at which intangible belongings can boost is smartly illustrated with the aid of actions within the hashish sector, where many of Lexaria's patents practice.

    The criminal world hashish market all started to emerge just recently, thanks to a handful of countries allowing clinical marijuana. The market has began to mature through a small however growing to be variety of territories with legal markets for leisure cannabis, as well as the separate management and licensing of industrial hemp. criminal hashish is now a multibillion-dollar company. youngsters Lexaria's know-how has other profound drug birth applications, cannabis is where it appears set to have the most instant have an effect on.

    This has resulted in a rush to manage intangible assets around hashish. it's noteworthy that chinese organizations own more than half of the cannabis-connected patents registered with the realm highbrow Property organization, although the hashish alternate continues to be unlawful in China. China has a pretty good eye for long-time period tendencies and is atmosphere itself up to control a turning out to be global exchange.

    backyard of China, Lexaria is likely one of the world's biggest holders of cannabis patents with eight patents already granted. The enterprise at the moment holds 4 patents in the u.s. and 4 in Australia, protecting aspects of its work to make a good suggestion drugs more palatable and effective. furthermore, the company has additional patent purposes in process in more than 40 international locations as part of a concerted strategy to extend its patent portfolio. here's made viable by way of the focused, tangible analysis and building being performed with the aid of the company and is subsequently driven via the deserve to improve and control intangible assets.

    Lexaria has disclosed it expects four greater patents granted in 2018, bringing its total to twelve. Of notice, all twelve of those are within only a single patent family unit - and Lexaria expects patent success across all 9 households of its current functions. certainly the enterprise currently revealed that it has seven extra patent families in the works. Lexaria's purpose is to have some 200 patents pending or granted which, if carried out, might turn the company into an IP behemoth in the world cannabis trade.

    The cost of Patents

    The business price of patents, even these still pending approval, will also be staggering, accomplishing billions of dollars. In 2011, Nortel sold 6,000 patents and patent applications for $four.5 billion. right here 12 months, AOL bought 925 patents to Microsoft for greater than one billion dollars. whereas some organizations strengthen patents simply to protect their work, others develop them specially to be bought.

    This center of attention on intangible belongings drives many mergers and acquisitions. The Craftsy digital network developed on-line indicates corresponding to "Man about Cake" with a focus on viewers in place of immediate profit, leading to the enterprise's acquisition via NBC frequent. The intangible belongings of the business's identification, popularity and viewers had turn into particularly effective in themselves.

    identical priorities will also be considered on a much larger scale in SoftBank's $31.four billion acquisition of chip dressmaker ARM Holdings, or roughly $7 million per patent or patent application. ARM's revenues had been simplest $1.5 billion per 12 months, but its four,500 patents cover expertise of potentially awesome price. in exactly three years, the need to manage patented medication and techniques in the healthcare sector standard resulted in fifty eight mergers and acquisitions with values of a thousand million greenbacks or extra.

    In Lexaria's container of prescribed drugs, patents are essential, representing each the outcomes of years of R&D and also expertise market share and handle within the emerging international cannabis business. Lexaria is trying to own satisfactory IP in the world hashish business that patent royalty revenues start to movement international.

    as a result of patents are among the many most constructive property a corporation can own, a small investment in analysis now might also pay off in a tremendous method in years to come. In a sector with such large growth potential as hashish, Lexaria's IP portfolio may additionally generate money flows even with even if other businesses intend to license the expertise or no longer; by using possessing core IP, different companies may well be forced to pay royalties. If Lexaria's goal of owning a whole lot of issued patents turns into a fact, the enterprise's patent portfolio could have huge ramifications on its valuation.

    Powered through highbrow Property

    Lexaria is never on my own in its commitment to strengthen its IP collateral. Tilray Inc. (NASDAQ:TLRY) is additionally specializing in building its IP portfolio including exclusive rights to at least 22 issued or pending patents. Tilray CEO Brendan Kennedy has followed that if hashish groups are going to flow forward, they'll should enhance IP in both the medicinal and exercise features of the hashish spectrum.

    GW prescribed drugs Plc (NASDAQ:GWPH) has based a number one position within the construction of plant-derived cannabinoid therapeutics via its intellectual property portfolio, proven drug discovery and building techniques, and regulatory and manufacturing skills. The enterprise efficiently developed the world's first prescription medicine derived from the cannabis plant.

    apart from the eight provisional patents it has filed, canopy growth supplier (NYSE:CGC) has up-to-date applications concerning past insomnia patent purposes, bringing the entire number of U.S. provisional patent filings to 39. The filings are part of a concerted plan by using cover growth to convey to patients and healthcare suppliers inventive medicines and fitness items concentrated on disease areas with mammoth scientific needs.

    Aurora hashish, Inc.'s (OTCQX:ACBFF) ache-management patents are simplest a part of the business's commitment to making a stronghold in the hashish market. Aurora has become probably the most quickest-becoming cannabis organizations, as evidenced by using Aurora Sky, the business's 800,000-rectangular-foot flagship facility; with the aid of launching the world's only cellular app for ordering medically prescribed cannabis; and by way of being the best LP to carrier two metropolitan areas with equal-day delivery.

    With so an awful lot price positioned on intangible belongings, patents are fueling boom and acquisitions value billions of dollars. in the pharmaceutical sector and past, patents really seem like where the huge money is.

    For greater information on Lexaria, please visit Lexaria Bioscience Corp. (CSE:LXX) (OTCQX:LXRP).

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    There are an increasing number of ways to do machine learning inference in the datacenter, but one of the increasingly popular means of running inference workloads is the combination of traditional CPUs acting as a host for FPGAs that run the bulk of the inferring.

    This is distinct from machine learning training, which at the moment is dominated by the combination of CPUs hosts and GPU accelerators, the latter of which do the parallel processing that runs the neural network and creates the model upon which inference is based. There are good reasons for these distinct architectures to be used.

    Deep learning consists of two phases: Training and inference. As illustrated in Figure 1 below, training involves learning a neural network model from a given training dataset over a certain number of training iterations and loss function. The output of this phase, the learned model, is then used in the inference phase to speculate on new data.

    The major difference between training and inference is that training employs forward propagation and backward propagation (two classes of the deep learning process) whereas inference mostly consists of forward propagation2. To generate models with good accuracy, the training phase involves several training iterations and substantial training data samples, thus requiring many-core CPUs or GPUs to accelerate performance.

    Figure 1. Deep Learning phases.

    After a model is trained, the generated model may be deployed (forward propagation only) e.g., on FPGAs, CPUs or GPUs to perform a specific business-logic function or task such as identification, classification, recognition and segmentation [see Figure 2 below].

    For this model deployment and inferencing stage, FPGAs are getting more and more attractive for Convolutional Neural Networks (CNN) because of their increasing floating-point operation (FLOP) performance, and secondly their support for both sparse data and compact data types. These trends are favoring FPGA-based platforms since FPGAs are designed to handle irregular parallelism and fine-grained computations compared to GPUs and CPUs. The focus of this tech note will be on FPGA as accelerated inference platform for CNNs.

    Figure 2. Inference Flow. Why Choose An FPGA Accelerator?

    FPGAs provide flexibility for AI system architects searching for competitive deep learning accelerators that also support differentiating customization. The ability to tune the underlying hardware architecture, including variable data precision, and software-defined processing allow FPGA-based platforms to deploy state-of-the-art deep learning innovations as they emerge. Other customizations include co-processing of custom user functions adjacent to the software-defined deep neural network. Underlying applications are in-line image and data processing, front-end signal processing, network ingest, and I/O aggregation.

    Figure 3. (Left) Arrayed building blocks are connected via interconnect wires; (Right) Fully featured FPGAs include a variety of advanced building blocks.

    Figure 3 above illustrates the variety of building blocks available in an FPGA. The core fabric implements digital logic with Look-up tables (LUTs), Flip-Flops (FFs), Wires, and I/O pads. FPGAs today also include Multiply-accumulate (MAC) blocks for DSP functions, Off-chip memory controllers, High-speed serial transceivers, embedded, distributed memories, Phase-locked loops (PLLs), hardened PCIe interfaces, and range from 1,000 to over 2,000,0000 logic elements.

    FPGAs In Mission Critical Applications

    Mission-critical applications (for example, autonomous vehicle, manufacturing, etc.) require deterministic low-latency. The data flow pattern in such applications may be in streaming form, requiring pipelined-oriented processing. FPGAs are excellent for these kinds of use cases given their support for fine-grained, bit level operations in comparison to CPU and GPUs. FPGAs also provide customizable I/O, allowing their integration with these sorts of applications.

    In autonomous driving or factory automation where response time can be critical, one benefit of FPGAs is that they allow tailored logic for dedicated functions. This means that the FPGA logic becomes custom circuitry but highly reconfigurable, yielding very low compute time and latency. Another key factor may be power – the cost per performance per watt may be of concern when determining long-term viability. Since the logic in FPGA has been tailored for a specific application/workload, the logic is very efficient at executing that application which leads to lower power or increased perf per watt. By comparison, CPUs may need to execute 1000’s of instructions to perform the same function that an FPGA maybe able to implement in just a few cycles.

    Figure 4. Examples of mission critical applications that require deterministic, fast response.

    The Intel Programmable Accelerator Card (PAC) features an Intel Arria 10 FPGA, an industry-leading programmable logic built on 20 nanometer process technology, integrating a rich feature set of embedded peripherals, embedded high-speed transceivers, hard memory controllers and IP protocol controllers. Variable-precision digital signal processing (DSP) blocks integrated with hardened floating point (IEEE 754 compliant) enable Intel Arria 10 FPGAs to deliver floating point performance of up to 1.5 teraflops. Arria 10 FPGAs have a comprehensive set of power-saving features. Combined, these features allow developers to build a versatile set of acceleration solutions.

    Figure 5. Intel Programmable Acceleration Card (PAC) Figure 6. PAC Block Diagram. The Intel FPGA Acceleration Stack

    The Acceleration Stack for Intel Xeon CPU with FPGAs is a robust collection of software, firmware, and tools designed and distributed by Intel to make it easier to develop and deploy Intel FPGAs for workload optimization in the data center. The Acceleration Stack for Intel Xeon CPU with FPGAs provides multiple benefits to design engineers, such as saving time, enabling code-reuse, and enabling the first common developer interface.

    It also provides optimized and simplified hardware interfaces and software application programming interfaces (APIs), saving developer’s time so they can focus on the unique value add of their solution.

    Figure 7. Acceleration Stack for Intel FPGAs. Intel OpenVINO

    When packaged with Intel OpenVINO toolkit, users have a complete top to bottom customizable inference solution.

    This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ inference engine API integrated with application logic. It is included in the OpenVINO toolkit and is also available as a stand-alone download. As shown in Figure 8 below, this toolkit comprises the following two components:

    A. Model Optimizer

    This is a Python-based command line tool that imports trained models from popular deep learning frameworks such as Caffe, TensorFlow, and Apache MXNet.

  • Runs on both Windows and Linux
  • Performs analysis and adjustments for optimal execution on endpoint target devices using static, trained models
  • Serializes and converts a model into an intermediate representation (IR) format from Intel
  • Supports over 100 public models for Caffe, TensorFlow, and MXNetInference Engine
  • B. Inference Engine

    This execution engine uses a common API to deliver inference solutions on any platform of choice: CPU, GPU, VPU, or FPGA.

  • Executes different layers on different targets (e.g., FPGA and selected layers on a CPU)
  • Implements custom layers on a CPU while executing the remaining topology on a FPGA – without having to rewrite the custom layers
  • Optimizes execution (computational graph analysis, scheduling, and model compression) for target hardware with an embedded-friendly scoring solution
  • Takes advantage of new asynchronous execution to improve frame-rate performance while limiting wasted processing cycles
  • Uses a convenient C++ API to operate on IR files and optimize inference
  • Figure 8. Intel’s deep learning deployment toolkit. Evaluation Methodology

    In this section, we give a brief overview of sample image-classification application, the hardware inferencing infrastructure used, and the deep learning models that were evaluated.

    Figure 9. End-to-end deep learning: from trained models to model deployment. Application Case Study

    ResNet is one the most widely used models for image recognition, known for its high accuracy, and in fact, the winning model of the ImageNet competition back in 2015. Compared to AlexNet (Table 1), RestNet-50 (a variant of the ResNet model) executes more than 60x operations for one image cycle, at an error rate of less than 3.5 percent versus 15 percent for AlexNet.

    Table 1: Comparing model architectures of ResNet-50 and AlexNet Figure 10. Image classification with CNNs.

    The application outputs various labels with varying confidence levels. Assuming the application was embedded in a mission-critical application requiring low-latency, what are the sort of things one would most importantly care about?

    Given some trained models, the aim is to deploy them for inference bearing latency, cost, and developer efficiency.

    The Dell PowerEdge Server

    We used the Dell PowerEdge R740/R740xd servers to host the PAC boards. The PowerEdge R740/R740xd is a general-purpose platform with highly expandable memory (up to 3TB) and impressive I/O capability to match both read-intensive and write-intensive operations. The R740 is capable of handling demanding workloads and applications such as data warehouses, E-commerce, databases, high-performance computing (HPC), and deep learning workloads.

    Figure 11. Dell PowerEdge R740/R740xd.

    The PowerEdge R740/R740xd is Dell EMC’s latest two-socket, 2U rack server designed to run complex workloads using highly scalable memory, I/O capacity, and network options. The R740/R740xd features the Intel Xeon processor scalable family, up to 24 DIMMs, PCI Express (PCIe) 3.0 enabled expansion slots, and a choice of network interface technologies to cover NIC and rNDC. In addition to the R740’s capabilities, the R740xd adds unparalleled storage capacity options, making it well-suited for data intensive applications that require greater storage, while not sacrificing I/O performance.

    Accelerating Inference

    As shown in Figure 12 below, our goal is simple.

  • Flexibly import models
  • Model optimization: by quantizing floating-point operations – which would lead to reduce model size (good for embedded), low energy utilization, also execute quicker
  • Deterministic low-latency by using FPGA-based accelerators like Intel PAC with Arria 10 FPGA
  • Productivity: simplified ease-of-use and streamlined development through Intel OpenVINO.
  • Figure 12: Accelerating Inference Putting It All Together

    For the complete solution, we roll up all these components (hardware, software, and developer toolkit) and a created a cluster based on Dell R740 server and Intel Xeon Scalable processors as shown in Figure 13 below:

    Figure 13. Complete inference solution: hardware, software, and accelerator. Performance Results

    We benchmarked SqueezeNet at batch sizes of 1 and 64 using FP11 precision. We are currently working closely with the Intel FPGA acceleration team in optimizing other models such as ResNet, GoogLeNet and VGG. These are some early results and Dell EMC is working closely with Intel in further optimizing these results. We will release these new optimizations using OPENVINO toolkit on Intel PAC as a follow-up to these early results. Take a look:

    Figure 14. Comparing deep learning models at FP-11 for batch sizes of 1 and 64 respectively.

    The performance gain with SqueezeNet architecture makes it attractive in streaming applications, particularly in low power, memory-constrained, embedded devices.

    Figure 15. Latency (turn-around time) for various model architectures.

    Another way to evaluate performance is through latency as plotted in Figure 15 above. We see with SqueezeNet it takes less than 1 millisecond as the total turnaround time to perform inference on a single image, that is., at batch size of 1. Again, the performance difference is less significant when running these models at batch size of 64. These sorts of analyses are useful when trading-off between inference time, model footprint and model accuracy at specific precisions, in this example, FP11.

    Results For Peak Performance/Watt

    Efficient energy utilization is important to minimize operational costs. In large-scale scenarios, for example, in the datacenters, the economics of scale is a crucial factor when deploying hardware accelerators on servers. Consequently, performance/watt becomes a key metric for normalizing performance. Since the PAC accelerator has a low power envelope (a thermal design point of 50 watts), it can achieve increased throughput/watt compared to CPUs or GPUs which are typically orders of magnitude higher in TDP. Table 2 below summarizes the energy efficiency of the PAC accelerator running SqueezeNet architecture:

    Table 2. Performance/watt of PAC when running SqueezeNet. Conclusions

    This tech note has presented an overview of deep learning and CNN inferencing, motivating the need for FPGAs as inferencing accelerators in mission-critical (low-batch, low-latency) applications. We then presented the Intel Programmable Acceleration Card (PAC) along with the developer toolkit (OpenVINO) for deploying FPGA-accelerated CNN models. Using this PAC accelerator as an add-in card to a Dell PowerEdge R740 server, we demonstrated how we deployed pre-trained CNN models with no additional programming effort. Our results highlight achieved throughput for batch sizes of 1 and 64, for popular CNN models. We also presented latency (or turn-around time) of these models, observing the lowest latency with SqueezeNet at batch size of 1. Since the PAC accelerator can be reconfigured with improved logic, we can expect that continued development of this new logic will not only improve performance, but will also enable different bit precisions (for example, FP8 rather than FP11) and additional CNN and RNN architectures like SSD and LSTM.

    Bhavesh Patel is a Dell EMC distinguished engineer and drives server architectures and technologies within the Server Advanced Engineering group.  In addition to his duties as a server architect, he is focused on machine learning/deep learning, high speed IO technologies and optical communications. Patel has served in various Engineering roles spanning from signal integrity to system design to architecture; he also hold patents in areas of high-speed I/O design, power sub-system and system architecture.


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    What is AI Face Search? Benefits over facial recognition systems

    When a child goes missing in a large, crowded mall, we have a panicking mom asking for help from the staff, at least a dozen cameras in the area, and assuming the child has gone missing for only 15 minutes, about 3 hours’ worth of video to look through to find the child. Typical security staff response would be to monitor the video wall while reviewing the footage and making a verbal announcement throughout the mall so the staff can keep an eye out for her. There is no telling how long it will take, while every second feels like hours under pressure. As more time passes, the possible areas where the child can be will widen, it becomes more time-consuming to search manually, and the likelihood of finding the child decreases. What if we can avoid all of that and directly search for that particular girl in less than 1 second? Artificial neural networks are improving every day and now enable us to search for a person across all selected camera streamsWith Artificial Intelligence, we can. Artificial neural networks are improving every day and now enable us to search for a person across all selected camera streams in a fraction of a second, using only one photo of that person. The photo does not even have to be a full frontal, passport-type mugshot; it can be a selfie image of the person at a party, as long as the face is there, the AI can find her and match her face with the hundreds or thousands of faces in the locations of interest. The search result is obtained in nearly real time as she passes by a certain camera. Distinguishing humans from animals and statues The AI system continuously analyses video streams from the surveillance cameras in its network, distinguishes human faces from non-human objects such as statues and animals, and much like a human brain, stores information about those faces in its memory, a mental image of the facial features so to speak. When we, the system user, upload an image of the person of interest to the AI system, the AI detects the face(s) in that image along with their particular features, search its memory for similar faces, and shows us where and when the person has appeared. We are in control of selecting the time period (up to days) and place (cameras) to search, and we can adjust the similarity level, i.e., how much a face matches the uploaded photo, to expand or fine-tune the search result according to our need. Furthermore, because the camera names and time stamps are available, the system can be linked with maps to track and predict the path of the person of interest. AI Face Search is not Face Recognition for two reasons: it protects people’s privacy, and it is lightweight Protecting people’s privacy with AI Face Search  All features of face recognition can be enabled by the system user, such as to notify staff members when a person of interest is approaching the store AI Face Search is not Face Recognition for two reasons: it protects people’s privacy, and it is lightweight. First, with AI Face Search, no names, ID, personal information, or lists of any type are required to be saved in the system. The uploaded image can be erased from the system after use, there is no face database, and all faces in the camera live view can be blurred out post-processing to guarantee GDPR compliance. Second, the lack of a required face database, a live view with frames drawn around the detected faces and constant face matching in the background also significantly reduces the amount of computing resource to process the video stream, hence the lightweight. Face Search versus Face Recognition AI Face Search Face Recognition Quick search for a particular person in video footage Identify everyone in video footage Match detected face(s) in video stream to target face(s) in an uploaded image Match detected face(s) in video stream to a database Do not store faces and names in a database Must have a database with ID info Automatically protect privacy for GDPR compliance in public places May require additional paperwork to comply with privacy regulations Lightweight solution Complex solution for large-scale deployment Main use: locate persons of interest in a large area Main use: identify a person who passes through a checkpoint Of course, all features of face recognition can be enabled by the system user if necessary, such as to notify staff members when a person of interest is approaching the store, but the flexibility to not have such features and to use the search tool as a simple Google-like device particularly for people and images is the advantage of AI Face Search.Because Face Search is not based on face recognition, no faces and name identifications are stored Advantages of AI Face Search Artificial Intelligence has advanced so far in the past few years that its facial understanding capability is equivalent to that of a human. The AI will recognise the person of interest whether he has glasses, wears a hat, is drinking water, or is at an angle away from the camera. In summary, the advantages of Face Search: High efficiency: a target person can be located within a few seconds, which enables fast response time. High performance: high accuracy in a large database and stable performance, much like Google search for text-based queries. Easy setup and usage: AI appliance with the built-in face search engine can be customised to integrate to any existing NVR/VMS/camera system or as a standalone unit depending on the customer’s needs. The simple-to-use interface requires minimal training and no special programming skills. High-cost saving: the time saving and ease of use translate to orders of magnitude less manual effort than traditionally required, which means money saving. Scalability: AI can scale much faster and at a wider scope than human effort. AI performance simply relies on computing resource, and each Face Search appliance typically comes with the optimal hardware for any system size depending on the customer need, which can go up to thousands of cameras. Privacy: AI Face Search is not face recognition. For face recognition, there are privacy laws that limits the usage. Because Face Search is not based on face recognition, no faces and name identifications are stored, so Face Search can be used in many public environments to identify faces against past and real-time video recordings. AI Face Search match detected face(s) in video stream to target face(s) in an uploaded image Common use cases of AI Face Search In addition to the scenario of missing child in a shopping mall, other common use cases for the AI Face Search technology include: Retail management: Search, detect and locate VIP guests in hotels, shopping centres, resorts, etc. to promptly attend to their needs, track their behaviour pattern, and predict locations that they tend to visit. Crime suspect: Quickly search for and prove/disprove the presence of suspects (thief, robber, terrorist, etc.) in an incident at certain locations and time. School campus protection: With the recent increase in number of mass shootings in school campuses, there is a need to identify, locate and stop a weapon carrier on campus as soon as possible before he can start shooting. Face Search will enable the authorities to locate the suspect and trace his movements within seconds using multiple camera feeds from different areas on campus. Only one clear image of the suspect’s face is sufficient. In the race of technology development in response to business needs and security concerns, AI Face Search is a simple, lightweight solution for airports, shopping centres, schools, resorts, etc. to increase our efficiency, minimise manual effort in searching for people when incidents occur on site, and actively prevent potential incidents from occurring. By Paul Sun, CEO of IronYun, and Mai Truong, Marketing Manager of IronYun


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    Nortel 920-533 Exam (Carrier VoIP - Communication Server 2000 - Compact) Detailed Information



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