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L50-501 LSI SVM5 Implementation Engineer

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L50-501 exam Dumps Source : LSI SVM5 Implementation Engineer

Test Code : L50-501
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: 119 Real Questions

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LSI LSI SVM5 Implementation Engineer

SAP and LSI Consulting help Small and Midsize Healthcare suppliers offer greater services at lessen can charge | killexams.com Real Questions and Pass4sure dumps

jointly Developed software solution for Healthcare is based on SAP(R) most suitable Practices programs to improvement Healthcare providers With confirmed Integration and Operations techniques

NEWTOWN square, Pa.-- The want for productive operations within the healthcare company trade isn't remoted to massive fitness programs and medical institution chains. Smaller and midsize healthcare issuer organizations are additionally under power to continuously turn into greater effective whereas on the equal time providing high-quality care. SAP AG (NYSE: SAP) in conjunction with LSI Consulting nowadays announced the provision of a healthcare-based solution that leverages the energy of the commercial enterprise useful resource planning (ERP) software SAP® ERP and SAP® most appropriate Practices packages for small to midsize hospitals in the U.S. market.

The industries group at SAP worked carefully with LSI to check the strategies and tactics of outdated successful SAP integrations at bigger clinical facilities, together with those connected to greater-researching associations working SAP applied sciences. The group then aggregated the finest practices recognized to construct the solution, which might be offered by the use of each the traditional on-premise and easy on-demand models. The software is designed for hospitals and scientific centers in the small to midsize segment, described as providing 400 beds or fewer, with a view to use it to achieve equivalent enterprise value in a shorter time duration and at a lower charge aspect than their larger counterparts.

As a depended on healthcare consultancy and implementation enterprise, LSI recognized the want for sophisticated expertise in these smaller, customarily community-based mostly facilities. however with restricted budgets and materials, small to midsize hospitals want guidance in securing an accelerated time to enterprise cost from their IT implementations, with bendy deployment alternatives, with out sacrificing satisfactory or a customer-focused method. the new solution turned into for this reason developed the use of the training learned from diverse on-time, on-funds implementations carried out by LSI. The outcomes is a portfolio of pre-packaged templates and accelerators that allow repeatable success via confirmed solutions that engineer lots of the time-intensive choices out of the implementation procedure.

"because of their long background of presenting SAP solutions to colossal healthcare carrier organizations, we're in a special place to identify and accumulate the optimum practices for implementation throughout a firm's core business approaches," pointed out Steve Roach, director of Healthcare options at LSI Consulting. "The solution they have now packaged is designed to handle the bottom set of primary functionalities obligatory by using hospitals enabling them to stay practicable within the smaller community setting. That performance comprises economic accounting, deliver chain management, together with alternate cart processing, and analytics."

a variety of Deployment models offer Flexibility in start

on account of the growing to be recognition and relevance of quite a few internet hosting methods, the brand new answer, which can also be purchased through LSI, should be offered through each an on-premise and on-demand mannequin. A hosted model is already purchasable, enabling for the answer itself to be managed, monitored and maintained through SAP experts in world-category hosted facts centers. This choice cuts down on the need for IT authorities to maintain the equipment on site, cutting back the capital rate burden that can avoid smaller companies from making the most of business-leading know-how. Licensing for a subscription-based mostly mannequin will become obtainable in coming months.

"SAP has a longtime popularity globally as a company of healthcare options to massive scientific facilities, and has worked in contemporary years to improve equally advisable solutions to small and midsize hospitals all over the world," spoke of John Papandrea, senior vice chairman, international health Sciences Sector, SAP. "The introduction of this jointly developed answer, principally configured to fulfill the wants of the U.S. market, suggests their dedication to this section as they observe top-quality practices, articulated inside a template, to present a comprehensive solution with bendy deployment alternatives that could mitigate chance and allow speedy time to price for smaller hospitals."

About LSI ConsultingDedicated to the U.S. Healthcare, larger schooling and analysis and Public Sector markets, LSI empowers organizations to maximise service, revenue, ROI and supply chain visibility for their valued clientele. With over a decade of experience as an SAP implementer, its mission is to support optimize their customers' company operations to force fulfillment of strategic organizational and public aims. LSI's complete offering of full lifecycle features contains preconfigured solutions for accelerated SAP Healthcare, better education, analysis and native executive implementations. To study greater, seek advice from http://www.lsiconsulting.com/.

About SAPSAP is the realm's main issuer of business software(*), offering applications and capabilities that allow organizations of all sizes and in additional than 25 industries to develop into premiere-run groups. With greater than ninety seven,000 shoppers in over a hundred and twenty international locations, the enterprise is listed on a couple of exchanges, together with the Frankfurt inventory alternate and NYSE, beneath the symbol "SAP." For extra information, seek advice from www.sap.com.

(*) SAP defines enterprise software as comprising commercial enterprise resource planning, enterprise intelligence, and linked purposes.

follow SAP on Twitter at @sapnews.

For consumers attracted to studying more about SAP items:world client middle: +49 one hundred eighty 534-34-24United States most effective: 1 (800) 872-1SAP (1-800-872-1727)

For extra counsel, press best:Dorit Shackleton, (604) 974-2444, dorit.shackleton@sap.com, PDTSAP Press office, +49 (6227) 7-46315, CET; +1 (610) 661-3200, EDT;press@sap.comBecca Hatton, Burson-Marsteller, (202) 530-4568, becca.hatton@bm.com, EDT

supply: SAP AG

web website: www.sap.com/

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LSI Industries Inc. F4Q09 (Qtr end 30/06/09) earnings name Transcript | killexams.com Real Questions and Pass4sure dumps

No outcomes found, are trying new key phrase!We had met with many consulting engineer ... for this market and LSI is the only company that they've considered that is able to do these things all together. i would say that business plan is in a position compl...

A Design Methodology for constructing technique-independent Hardmacro IP | killexams.com Real Questions and Pass4sure dumps

unexpectedly migrating highbrow property (IP) from one foundry to an additional and from one process node to the next will also be a challenging, however fundamental, a part of the business, in particular if the IP is generated and delivered as hardmacro IP.  That’s because challenging IP or a GDSII netlist versus soft IP, RTL or gate-level netlist ought to be available for all most important foundries and for a wide selection of system nodes at every foundry.

Designing new memory IP is essentially a guide assignment that comprises extra analog circuits —can charge pumps, voltage regulators and experience amplifiers, for instance — than most good judgment designs.  At four megabytes (Mb), the design burden facing the layout supervisor and his implementation group is formidable. 

These concerns are commonplace of reminiscence design, and the Kilopass team ran into them when designing a contemporary 4-Mb memory IP block.  Its adventure offers approaches in which different groups can overcome these challenges.

To ease the burden, the team identified points of the design that may well be computerized, liberating them to be aware of crucial analog aspects that should be tailored for a person foundry and technique.  The outcomes is an electronic design automation (EDA) tool move developed for the newest anti-fuse non-risky memory (NVM) product line. 

Exploiting the EDA tool circulate

using commercially obtainable EDA options, the primary task to be automatic become fast and accurate entry of design ideas, together with managing design intent in a means that flowed naturally in the schematic.  This allowed designers to visualize and consider the numerous interdependencies of an analog or combined-sign design and its consequences on circuit performance.

In designing this four-Mb reminiscence, about 10 % of the design required the layout team to import a GDSII netlist developed by an extra internal team.  The project of merging the existing layout with the relaxation of the reminiscence design become an easy manner requiring a day of manual location and route or connecting I/Os and routing vigour and ground. 

The team made use of the potential to embed design constraints in the netlist passed between the front-conclusion design group and the implementation crew.  Up except that factor, these constraints had been communicated with the aid of cryptic notes on the schematic.  for instance:  Specify that two gates mandatory to be matched or a selected web is critical and its optimum size have to now not be handed in the layout.  Communicated the historical method, when the schematic became given to the implementation engineer, the clothier could simplest hope that the implementation crew executed the preferred outcome. 

Managing Complexity

The implementation crew built a new memory through making a single reminiscence cell then replicated it to construct the extra advanced arrays — a backside-up approach versus the standard proper-down method that gigantic digital SoC design teams make use of.  The memory consisted of a little mobile comprising two transistors.  

once the bit cellphone became created and optimized, the cell turned into replicated along a horizontal line of length n (32, for instance), as specific within the schematic.  once the line of reminiscence cells became created, it become replicated vertically m variety of rows (32, as an instance), as distinctive in the schematic.  in this method, the implementation engineer created a 1-Mb memory array.  The procedure turned into repeated three greater times to create the 4-Mb reminiscence, a two-with the aid of-two matrix of 1-Mb arrays.

In constructing the initial netlist, the front-conclusion fashion designer labeled each and every of the bit cells and its linked vigour, floor, bit line and note line.  He then specific the variety of bit cells to be powered by using a given vigor net to ensure that each and every phone received the same amount of latest.

The potential to set constraints in the netlist passed to the implementation engineer ensured the design intent was captured in remaining design. 

Automating Repetitive guide projects

Automating repetitive initiatives significantly reduced the time to comprehensive the design as smartly.  as an instance, labeling the bit line, observe line, feel amplifier, and vigor and floor for a four-Mb memory can take considerable time if achieved manually.  Surrendering the assignment to the EDA tool reduced the chore to a handful of keyboard operations.

In a pull-down menu, the implementation engineer achieved a kind that requested for the sign name, number of pins to be labeled, x and y coordinates of the primary signal, spacing between pins, and the size of the textual content.  From this advice, the design device created each and every particular person sign identify.

as soon as the reminiscence IP become applied for the forty-nanometer (nm) manner at foundry A, the design crew changed into confronted with changing the reminiscence to 40 nm at another foundry.  With the automatic EDA circulation, this became reduced to a two-step procedure:  layer mapping followed by means of statistics manipulation.  within the first step, the mapping desk for foundry A’s forty-nm library was replaced with the mapping table for foundry B’s 40-nm library.  The device then automatically produced a design for foundry B.  The process turned into now not completely automatic, as each and every foundry has its own wonderful guidelines; for that reason exceptions that do not map one-to-one are highlighted.

The implementation engineer examined all generated exceptions then made integral alterations.  as an example, in foundry A, the bit telephone might also use layer C and D, whereas in foundry B, layer D and E are used.  once the conversion became accomplished, a design rule check (DRC) changed into carried out together with ultimate verification. 

This automation took half the time in comparison with starting from scratch, enabling quick migration of IP from one foundry to a further.  while designing new memory IP, comparable to Kilopass’ Gusto anti-fuse NVM, remains on the whole a guide effort, features may also be automatic, enabling the layout and implementation team to concentrate on critical analog aspects that have to be tailor-made for an individual foundry and procedure.  

author Bio:  Bernd Stamme is Director for advertising and marketing and functions at Kilopass expertise.  He has more than 15 years of adventure within the IP and semiconductor trade. just before Kilopass, he become the Director of IP know-how at SiRF technology managing the licensing and a success integration of third-celebration IP into SiRF’s GPS chip sets.  before SiRF, he held administration positions in LSI logic’s CoreWare company and worked on excessive-pace SerDes IP, conversation interfaces and processor core.  Stamme holds a Dipl.-Ing. diploma in Electrical Engineering from FH Bielefeld in Germany.


L50-501 LSI SVM5 Implementation Engineer

Study Guide Prepared by Killexams.com LSI Dumps Experts


Killexams.com L50-501 Dumps and Real Questions

100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



L50-501 exam Dumps Source : LSI SVM5 Implementation Engineer

Test Code : L50-501
Test Name : LSI SVM5 Implementation Engineer
Vendor Name : LSI
: 119 Real Questions

attempt out these actual L50-501 questions.
Passed the L50-501 exam with 99% marks. Excellent! considering only 15 days preparation time. All credit goes to the Question & Answer by killexams. Its amazing material made preparation so easy that I could even understand the hard topics at ease. Thanks a lot, killexams.com for providing us such an easy and effective study guide. Hope your team keep on creating more of such guides for other IT certification tests.


I need dumps contemporary L50-501 examination.
My exam readiness came about into 44 right replies of the aggregate 50 in the planned 75 minutes. It worked just simply the great. I got an attractive experience depending on the killexams.com dumps for the exam L50-501. The aide clarified with compact answers and reasonable cases.


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found maximum L50-501 Questions in real exam that I prepared.
L50-501 QAs have stored my life. I didnt sense confident in this location and Im happy a friend has knowledgeable approximately killexams.com LSI package with me a few days earlier than the examination. I need id buy earlier, it would have made matters a whole lot less difficult. I notion that I passed this L50-501 examination very early.


No worries whilst getting geared up for the L50-501 examination.
i am over the moon to mention that I exceeded the L50-501 exam with ninety two% rating. killexams.com Questions & answers notes made the entire issue substantially easy and clean for me! maintain up the notable work. inside the wake of perusing your direction notes and a chunk of practice structure exam simulator, i used to be efficiently ready to bypass the L50-501 examination. clearly, your direction notes in reality supported up my actuality. a few topics like instructor conversation and Presentation abilties are achieved very nicely.


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I had bought your online mock test of L50-501 exam and feature passed it in the first try. im very lots grateful to you in your support. Its a delight to inform that ive passed the L50-501 examination with seventy nine% marks..thank you killexams.com for the whole thing. You men are virtually wondeful. Please maintain up the good paintings and hold updating the cutting-edge questions.


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Video Encoding: Go for the Specialist or the Jack-of-All-Trades? | killexams.com real questions and Pass4sure dumps

Video Encoding: Go for the Specialist or the Jack-of-All-Trades?

When it comes to video encoding, the choice between hardware and software comes down to flexibility, latency, and cost.

Page 1

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One of the hardest choices encoding technicians have to make is deciding between hardware and software. Hardware-based encoders and transcoders have had a performance advantage over software since computers were invented. That's because dedicated, limited-purpose processors are designed to run a specific algorithm, while the general-purpose processor that runs encoding software is designed to handle several functions. It's the specialist versus the jack-of-all-trades.

In the past few years, processors and workflows have changed. The great disruptor has been time and the economics of Moore's Law, which famously says that the number of transistors incorporated in a chip will approximately double every 24 months. The logical outcome of Moore's law is that the CPUs get more powerful by a factor of two every few years, but more recently processing power seems to double every few months. Lately, Intel -- whose co-founder Gordon Moore coined Moore's Law -- has been adding specialty functions along with its math co-processors to equalize the differences between general-use processors and specialty processors.

There are many layers and elements to both a general-purpose processor and a task-specific hardware processor. The general-purpose CPU is the most common -- there are literally billions of them in all manner of computing devices -- while the more purpose-oriented processors include digital signal processors (DSPs), field-programmable gate arrays (FPGAs), and integrated circuits (ICs) that are available for various industrial appliances and widely used in cellphones. Many of the structures and elements are similar across all types, but there are considerable differences. If you are not familiar with the elements of the various types, here are the basic structures of both.

The General-Purpose CPU

The general-purpose CPU is laid out with flexible core elements as the arithmetic logic unit (ALU), control unit (CU), and accessory elements that offer extra features for performance. Basically these two cores talk to each other, bring in memory as needed, and send work to the other elements. Other elements include I/O processors, logic gates, integrated circuits, and -- on most newer processors and especially on the Intel Xeon processors -- a beefy math co-processor. The math co-processor assists the ALU and can handle the more extreme and complex mathematical computations. Essentially, it gives the processor the extra horsepower it might require.

The Dedicated Processor

Specific-purpose hardware encoders have been around longer than general-purpose processors, and the latter have been slower at mathematical equations or algorithm problems. History is vitally important to understand the market and technology, not to mention get a sense of what the future holds. The earliest example of encoding was in 1965, when the Intelsat 1 (Early Bird) became the first commercial deployment of a satellite to downlink video and audio. Since then, the world has been using specific processors to process video, and the technology has made leaps and bounds to offer higher density and quality.

Video ARM DSP

This is a general layout of a video ARM DSP. The ARM core runs the embedded operating system, working like a traffic cop to control input and output. 

Dedicated processors -- such as dedicated signal processors (DSPs), graphics processing units (GPUs), and vector processors -- all have a very similar design structure. A basic and most common element is an I/O manager, which has a tiny onboard operating system along with memory. This is the traffic cop, controlling input and output. Then there are multiple specialized processing modules that execute the desired instructions very quickly and that DSPs and other dedicated processors support. Unlike a general-purpose processor, which has many possible general instructions that may not be most efficient for the task at hand, dedicated processors rely on accelerated, per-function instructions that are more job-specific.

Dedicated processors and encoders have a variety of applications and workflows. If you look at the major users of professional encoders, you will see that in many cases they rely on specialized encoders. The following well-known companies make DSP chips: LSI Corp.; Texas Instruments, Inc.; Analog Devices, Inc.; Sony; and Magnum Semiconductor. These DSPs are used in devices such as media gateways, telepresence devices, cellphones, and military and radar processing.

FPGAs are now very popular implementations of DSP functions because of their flexibility of setup and upgrade. Since they are field-upgradeable, their development costs for the user are significantly cheaper than DSPs and application-specific integrated circuits (ASICs; more on those in a moment) from the traditional DSP providers. You can see FGPAs from Altera Corp. and Xilinx, Inc. that have DSP functions built in. If you want a board or system that is easier to upgrade in the field, then this is probably the best way to go.

DSP Packets

While each manufacturer will tweak its design slightly, this is an overview of how communications and packets flow into the modules of DSPs and ASICs. 

Another implementation of the dedicated processor is the ASIC. These factory-programmed DSPs are used everywhere cost is a crucial consideration, because they offer special functionality at optimal price and performance. In general, they are more expensive to design but are cost-effective for any appliance or board manufacturer to implement into their systems. Many manufacturers of DSPs also manufacture ASICs; companies such as NXP Semiconductors, Broadcom Corp., and Freescale, Inc. also make custom ASIC DSPs.

If you ever open up a hardware encoding appliance -- from IP media gateways to broadcast encoder and decoders -- you will see several of the chips previously listed. You can find appliances for every industry. Today you will find a dedicated hardware-based encoding device from Harmonic, Inc.; Harris; Tandberg Data; or NTT Communications in every TV station or cable TV headend, and you'll find appliances or cards from ViewCast Corp. or Digital Rapids in many hybrid encoding farms, since they accelerate some of the functions in the hardware. If you've watched a video on YouTube, then you have seen video encoded by RGB Networks with the RipCode equipment, which used massive numbers of Texas Instruments, Inc. DSPs.

Pros and Cons of Hardware Encoders

There are always some pros and cons when it comes to specific design hardware encoders and dedicated appliances. The dedicated hardware approach with DSPs or chips is the perfect solution for media gateways and in low latency military applications. They are designed to run 24/7 with little or no human interaction. There are some processors that can encode an entire frame in the 1ms-10ms (millisecond) range and FPGAs that can encode in the 10ms-30ms range. These processes allow for the creation of appliances where the encoding latency is less than 100ms from encode to transmission to decode. Right now you can only get low latency using the right DSPs, ASICs, and FPGAs. The average lifespan of an appliance is 5-10 years depending on the configuration and manufacturer. Similar lifetimes are assumed for systems that rarely change, such as satellite uplink or cable system encoders.

The primary drawback of dedicated hardware-based encoding is that the codec on the processor is generally impossible to upgrade. Every DSP, ASIC, or FPGA is based on an algorithm that was finalized years ago. By the time the chip is ready to be sold, the codec is 6 months to a year old. Add more design cycles for the appliance development and manufacturing, and the end result is a device based on a codec that's a year or more old. If improvement to the codec comes out, the chip or device might never be able to integrate the new codec or technology due to the manufacturer or the way the chip was designed. The dedicated DSP approach can save a lot of money, but at the expense of flexibility. Those chips do just what they were originally designed to do and nothing more.

Hardware Encoder

Video comes into a hardware encoder to a media gateway, which will make adjustments to the video stream to address network conditions and the end user’s video decoding device. When done, it will send these modifications to the video decoder. 

There's another issue with dedicated chip-based encoders: Who determines the quality of the codes and streams? Is it a DSP engineer, a compressionist, or the producer and director? In a TV station, it's usually a combination of chief engineer and executive producer who decide what station image goes over the air. If they use a hardware encoder, in many cases decisions about encoding parameters have been taken out of their hands. The broadcast engineer has to work within the parameters the chip manufacturer has allowed end users to change, meaning that while there is usually some control, there may not be as much as a producer or engineer would like. There are only so many operations and cycles you can put on a chip, so some functionality is uneconomical to implement.

The Pros and Cons of Software Encoders

General-purpose CPUs share some similarities and architecture with dedicated processors. They are designed to handle the everyday functions of your PC or server, and they are optimized to do mundane tasks such as word processing. This is why your motherboard has a powerful graphics card in your machine; it's a specialty function that is best offloaded to a specific-designed processor. If you do any nonlinear video editing, you likely have a capture card with some specialty processors to give you real-time output or transitions.

In the encoding and streaming industry, they mostly use a capture board and one or more of many available software encoding packages. There are algorithms and formulas for every application, from live encoding to file-based transcoding to software-based decoding. These days most software-based encoders have hooks in the code to offload certain elements to accelerate or allow multiple CPUs to run parallel functions to get the best performance and quality. More recently, Intel is offering some onboard GPUs that feature decoding with MPEG, analysis of a video stream's motion vectors, and other functions.

GPU

This overview shows how a GPU or video accelerator is laid out. Again, one device works as a traffic cop to send work to the appropriate processors, then takes the video streams back and reassembles them together, allowing video to be encoded at a faster rate. 

Software encoders have allowed users to be much more flexible in responding to the needs of specific customers or events, and they all use the same general-purpose processors and capture boards to support more video formats and standards. This has been an advantage for software encoders for a long time. They are easy to reconfigure and use.

The software encoding industry has recently seen battles between open source and closed source. There are some notable pioneering closed source companies that helped drive the development of software encoding and streaming: Microsoft; Real Networks, Inc.; Sorenson Communications; and Adobe Systems, Inc. laid out the framework for modern streaming and web-based video. They have been around since the beginning, and in many cases they financed the codecs that became standards.

In addition to these pioneering companies, there has been a recent movement to open source. Some of the earlier versions such as x264 and the open source library in the University of California-Berkeley provide the foundation for most software encoders. Code is added every so often and allows others to program their custom apps. The better-known ones such as VideoLan (VLC), FFmpeg, and WebM are creating new versions and are catching on in general use. Some are even getting funding from some of the larger public companies. The most notable example is WebM, which is being funded mostly by Google, which made the VP8 codec open source after it acquired On2 Technologies. All this competition and activity is creating better products for consumers. The big companies realize open source development and innovation is faster-moving than their own, allowing the market to grow more quickly than it otherwise might.

But software-based encoding has some drawbacks. The most important parameters of encoding are quality, flexibility, price, latency, and support.

Software encoding's greatest advantages over pure hardware encoders are its flexibility and quality. Software has always been able to adopt and update incredibly fast. When new codec optimizations come out, encoding package updates follow very soon after.

Software encoding can enable the producer, engineer, or other user to get precisely the quality and image that they want, unlike the automated hardware solution, where the user has no say in what the overall image is and outcome will be. Some larger encoding firms hire color consultants and compressionists, along with programmers and delivery experts, all of whom help the executive producers and directors determine what the overall outcome should look like. It's a broadcast approach for streaming.

Xeon Phi

Later this year or in early 2013, Intel will release its Xeon Phi series of massive parallel coprocessors, which will work with existing Xeon processors and workflows. 

So if software encoding wins in flexibility and quality, what about speed or latency? While some highly tuned hardware encoders offer a latency down in the 30ms range, most software solutions run in the 300ms-500ms range, if not higher. Most people who use software encoding realize they are sacrificing some speed for quality. All that matters is whether or not they can get the resolution and framerate they want; if it's delayed some, the workflow can be designed to accommodate it. On the other hand, if you require the lowest latency and fastest delivery, you will have to give up some quality.

Cost of support is of course an important issue. Will the proprietary company keep making the version you're using, or is there a chance it will be withdrawn from the market? How much will the updates and upgrades cost? It turns out that upgrades in the open source community are relatively frequent, whereas upgrades in propriety software are less so.

While some people assume that open source products offer lower quality or reliability than proprietary software, that's not necessarily the case. FFmpeg, VLC, and WebM are all significantly upping their quality. On the other hand, proprietary software packages such as Sorenson and MainConcept have also stood the test of time and continue to find widespread use. Interestingly, MainConcept and Sorenson are two of the few companies whose solutions are used in both software and hardware encoding; both provide codecs for the PC environment as well as specifically designed chips.

Changes in Media Consumption, Changes in Media Encoding

General-purpose hardware-based decoders are now playing an important role in the overall media viewer world, especially as more and more viewers are quitting cable and going the IP route for all of their video consumption. Roku, Boxee, and other IP set-top boxes are DSP-based decoders. At the same time, more and more consumers are adopting Android or iOS devices and using them as personal media players, and each device brings with it its own set of ideal encoding profiles and parameters. You'll find you need to do custom scaling and probably want to offer the highest possible complexity. Then again, you need to spend more CPU cycles per frame, which will require more encoding time but create a better outcome.

Conclusion

There will always be a battle between hardware encoding and software encoding. Who will win in various market segments? Why a hardware encoder versus the software encoder? Even now they are starting to see more specialty functionality appended on the general-purpose CPUs, due to the miniaturization and density of transistors and processors. For instance, Intel recently agreed to buy 190 patents and 170 patent applications from RealNetworks, and for years the company has been adding graphics processing and other accelerators or processing engines.

Dedicated hardware encoding wins in unique parallel processing situations when massive amounts of data need to be processed, as well as in low latency communications such as real-time financial and some military applications. It also leads in situations where you want to just install the encoding tool and let it do its thing, such as in situations with YouTube that can rely on automated, predefined resolutions and bitrates for a massive amount of viewers. But software encoders will be the tool of choice in most applications. It's faster and cheaper to encode with software than in hardware, and once you see how the market responds to your output, it's faster and cheaper to make modifications.

So, do you most value flexibility and lower costs? Then software is probably your best bet. Do you need low latency and stream density or automated auto-transcoding for the mobile market? Then a hardware solution probably is best for you.

This article appears in the October/November, 2012, issue of Streaming Media magazine under the title "The Specialist Vs. the Jack-of-All-Trades."

Page 1

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A Design Methodology for Building Process-Independent Hardmacro IP | killexams.com real questions and Pass4sure dumps

Rapidly migrating intellectual property (IP) from one foundry to another and from one process node to the next can be a challenging, but necessary, part of the business, especially if the IP is generated and delivered as hardmacro IP.  That’s because hard IP or a GDSII netlist versus soft IP, RTL or gate-level netlist must be available for all major foundries and for a wide selection of process nodes at each foundry.

Designing new memory IP is largely a manual task that involves more analog circuits —charge pumps, voltage regulators and sense amplifiers, for example — than most logic designs.  At four megabytes (Mb), the design burden facing the layout manager and his implementation team is formidable. 

These issues are typical of memory design, and the Kilopass team ran into them when designing a recent 4-Mb memory IP block.  Its experience offers ways in which other teams can overcome these challenges.

To ease the burden, the team identified elements of the design that could be automated, freeing them to concentrate on critical analog elements that must be tailored for an individual foundry and process.  The result is an electronic design automation (EDA) tool flow developed for the latest anti-fuse non-volatile memory (NVM) product line. 

Exploiting the EDA Tool Flow

Using commercially available EDA solutions, the first task to be automated was rapid and accurate entry of design concepts, including managing design intent in a way that flowed naturally in the schematic.  This allowed designers to visualize and understand the many interdependencies of an analog or mixed-signal design and its effects on circuit performance.

In designing this 4-Mb memory, about 10 percent of the design required the layout team to import a GDSII netlist developed by another internal team.  The task of merging the existing layout with the rest of the memory design was a simple process requiring a day of manual place and route or connecting I/Os and routing power and ground. 

The team made use of the ability to embed design constraints in the netlist passed between the front-end design team and the implementation team.  Up until that point, those constraints were communicated by cryptic notes on the schematic.  For example:  Specify that two gates needed to be matched or a specific net is critical and its maximum length must not be exceeded in the layout.  Communicated the old way, when the schematic was given to the implementation engineer, the designer could only hope that the implementation team achieved the desired result. 

Managing Complexity

The implementation team built a new memory by creating a single memory cell then replicated it to build the more complex arrays — a bottom-up approach versus the conventional top-down approach that large digital SoC design teams employ.  The memory consisted of a bit cell comprising two transistors.  

Once the bit cell was created and optimized, the cell was replicated along a horizontal line of length n (32, for example), as specified in the schematic.  Once the line of memory cells was created, it was replicated vertically m number of rows (32, for example), as specified in the schematic.  In this manner, the implementation engineer created a 1-Mb memory array.  The process was repeated three more times to create the 4-Mb memory, a two-by-two matrix of 1-Mb arrays.

In developing the initial netlist, the front-end designer labeled each of the bit cells and its associated power, ground, bit line and word line.  He then specified the number of bit cells to be powered by a given power net to ensure that each cell received the same amount of current.

The ability to set constraints in the netlist passed to the implementation engineer ensured the design intent was captured in final layout. 

Automating Repetitive Manual Tasks

Automating repetitive tasks greatly reduced the time to complete the layout as well.  For example, labeling the bit line, word line, sense amplifier, and power and ground for a 4-Mb memory can take considerable time if done manually.  Surrendering the task to the EDA tool reduced the chore to a handful of keyboard operations.

In a pull-down menu, the implementation engineer completed a form that asked for the signal name, number of pins to be labeled, x and y coordinates of the first signal, spacing between pins, and the size of the text.  From this information, the design tool created each individual signal name.

Once the memory IP was implemented for the 40-nanometer (nm) process at foundry A, the layout team was confronted with converting the memory to 40 nm at another foundry.  With the automated EDA flow, this was reduced to a two-step process:  layer mapping followed by data manipulation.  In the first step, the mapping table for foundry A’s 40-nm library was replaced with the mapping table for foundry B’s 40-nm library.  The tool then automatically produced a layout for foundry B.  The process was not completely automated, as each foundry has its own unique rules; thus exceptions that do not map one-to-one are highlighted.

The implementation engineer examined all generated exceptions then made necessary adjustments.  For example, in foundry A, the bit cell may use layer C and D, whereas in foundry B, layer D and E are used.  Once the conversion was completed, a design rule check (DRC) was performed along with final verification. 

This automation took half the time compared with starting from scratch, enabling rapid migration of IP from one foundry to another.  While designing new memory IP, such as Kilopass’ Gusto anti-fuse NVM, continues to be mostly a manual effort, elements can be automated, enabling the layout and implementation team to concentrate on critical analog elements that must be tailored for an individual foundry and process.  

Author Bio:  Bernd Stamme is Director for Marketing and Applications at Kilopass Technology.  He has more than 15 years of experience in the IP and semiconductor industry. Prior to Kilopass, he was the Director of IP Technology at SiRF Technology managing the licensing and successful integration of third-party IP into SiRF’s GPS chip sets.  Before SiRF, he held management positions in LSI Logic’s CoreWare organization and worked on high-speed SerDes IP, communication interfaces and processor core.  Stamme holds a Dipl.-Ing. Degree in Electrical Engineering from FH Bielefeld in Germany.


Android's "Treble" interface | killexams.com real questions and Pass4sure dumps

[Posted May 12, 2017 by corbet]

The Android Developers Blog carries an announcement for an upcoming feature called "Treble", which looks like a separate, guaranteed stable interface for device drivers. "The core concept is to separate the vendor implementation - the device-specific, lower-level software written in large part by the silicon manufacturers - from the Android OS Framework. This is achieved by the introduction of a new vendor interface between the Android OS framework and the vendor implementation." Details are scarce, and there is no information on how this might fit into the part of the "Android OS framework" that many of us think of as "the Linux kernel". (Log in to post comments)


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References :


Dropmark : http://killexams.dropmark.com/367904/12855207
Dropmark-Text : http://killexams.dropmark.com/367904/12948656
Blogspot : http://killexams-braindumps.blogspot.com/2018/01/pass4sure-l50-501-dumps-and-practice.html
Wordpress : https://wp.me/p7SJ6L-2Qk






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