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Killexams : IBM Architect student - BingNews https://killexams.com/pass4sure/exam-detail/C2090-102 Search results Killexams : IBM Architect student - BingNews https://killexams.com/pass4sure/exam-detail/C2090-102 https://killexams.com/exam_list/IBM Killexams : How one research center is driving AI innovation for academics and enterprise partners alike

A new research center for artificial intelligence and machine learning has sprung up at the University of Oregon, thanks to a collaboration between IBM and the Oregon Advanced Computing Institute for Science and Society. The Oregon Center for Enterprise AI eXchange (CE-AIX) leverages the university's high-performance computing technology and enterprise servers from IBM to create new training opportunities and collaborations with industry.

"The new lab facility will be a valuable resource for worldwide universities and enterprise companies wanting to take advantage of IBM Enterprise Servers POWER9 and POWER10 combined with IBM Spectrum storage, along with AIX and RHEL with OpenShift," said Ganesan Narayanasamy, IBM's leader for academic and research worldwide.

Narayanasamy said the new center extends state-of-the-art facilities and other Silicon Valley-style services to researchers, system developers, and other users looking to take advantage of open-source high-performance computing resources.  The center has already helped thousands of students gain exposure and practice with its high-performance computing training, and it is expected to serve as a global hub that will help prepare the next generation of computer scientists, according to the center's director Sameer Shende.

"We aim to expand the skillset of researchers and students in the area of commercial application of artificial intelligence and machine learning, as well as high-performance computing technologies," Shende said.

Thanks to a long-term loan agreement with IBM, the center has access to powerful enterprise servers and other capabilities. It was envisioned to bring together data scientists from businesses in different domains, such as financial services, manufacturing, and transportation, along with IBM research and development engineers, IBM partner data scientists, and university students and researchers.

The new center also has the potential to be leveraged by everyone from global transportation companies seeking to design more efficient trucking routes to clean energy firms looking to design better wind turbines based on models of airflow patterns. At the University of Oregon, there are potential applications in data science, machine learning, environmental hazards monitoring, and other emerging areas of research and innovation.

"Enterprise AI is a team sport," said Raj Krishnamurthy, an IBM chief architect for enterprise AI and co-director of the new center. "As businesses continue to operationalize AI in mission-critical systems, the use cases and methodologies developed from collaboration in this center will further promote the adoption of trusted AI techniques in the enterprise."

Ultimately, the center will contribute to the University of Oregon's overall research excellence, said AR Razdan, who serves as the university's vice president for research and innovation.

"The center marks another great step forward in [the university's] ongoing efforts to bring together interdisciplinary teams of researchers and innovators," Razdan said.

This post was created by IBM with Insider Studios.

Sun, 24 Jul 2022 12:00:00 -0500 en-US text/html https://www.businessinsider.com/sc/how-one-tech-partnership-is-making-ai-research-possible
Killexams : Serverless Architecture Market Is Projected To Expand At A CAGR Of 27.8% By 2025

(MENAFN- EIN Presswire)

Serverless Architecture Industry

Rise in adoption of cloud technologies & the emergence of serverless computing in growing IoT landscape are expected to be opportunistic for the global market.

PORTLAND , PORTLAND, OR, UNITED STATE, July 14, 2022 /EINPresswire.com / -- Surge in number of smartphones, increase in BYOD adoption, rise in number of applications, growing shift from DevOps to serverless computing, and rising need to eliminate server management challenges have led to significant growth of the global serverless architecture market .

However, issues associated with third-party APIs restrict the market growth. On the other hand, emergence of serverless architecture applications in growing IoT landscape and growing cloud infrastructure services market would provide lucrative opportunities for the serverless architecture market.

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The public cloud segment accounted for nearly three-fourths of the total market share in 2017, and is expected to maintain its dominance by 2025. The major factors that drive the growth of this segment include its high availability, cost-efficiency and capabilities to Improve the functionality as well as overall development process. However, the private cloud segment is estimated to project the fastest CAGR of 30.0% from 2018 to 2025, owing to its less vendor-locking problems and enhanced security.

Based on applications, the web application development segment held nearly half of the total market share in 2017, and will maintain its dominance throughout the forecast period. This is due to serverless computing that allows developing and running of an application without the servers. This reduces the complex procedures such as planning capacity of the application, installation of hardware, procurement, and software.

However, the IoT backend segment is estimated to register the highest growth rate with a CAGR of 31.7% from 2018 to 2025, owing to growing IoT industry and increasing number of data sets associated with these connected devices.

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North America region accounted for nearly half of the market in terms of revenue in 2017. However, the Asia Pacific region is expected to grow at the highest CAGR of 31.0% during the forecast period. The research also analyzes regions including Europe and LAMEA.

Leading market players analyzed in the research include the Amazon Web Services, Alibaba Group, Google LLC, Oracle Corporation, Microsoft Corporation, IBM Corporation, Platform9 Systems, Inc., Twilio, Rackspace Inc., and Tibco Software.

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Thu, 14 Jul 2022 15:51:00 -0500 Date text/html https://menafn.com/1104534458/Serverless-Architecture-Market-Is-Projected-To-Expand-At-A-CAGR-Of-278-By-2025
Killexams : Global Business Programs

Global Business Programs provide students with exciting opportunities to expand their education to include business instruction in an international setting.  Participation in a Global Business Program helps to prepare students to work and compete in an international business environment. Developing an understanding of the world beyond the boundaries of the United States will be a huge advantage as you begin your career.

You are welcome to participate in a Global Business Program if you are a non-business student. Global Business Experiences (UNIV399) satisfies knowledge areas Contemporary and Global (CGI) and Cultures and Society (CSO).

View all of Clarkson's Global Opportunities
 

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Travel Warnings are different from Consular Information sheets which are available for every destination country. Students should always consult State Department consular information sheets for countries in which they plan to travel or study before finalizing plans to go there. These consular information sheets contain more detailed safety and security information, as well as other important health, political, and other information that students should be familiar with. Consular information sheets are also posted by the U.S. State Department.

Thu, 16 Nov 2017 17:37:00 -0600 en text/html https://www.clarkson.edu/gbp
Killexams : Political Chips

Last Friday AMD surpassed Intel in market capitalization:

Intel vs AMD market caps

This was the second time in history this happened — the first was earlier this year — and it may stick this time; AMD, in stark contrast to Intel, had stellar quarterly results. Both stocks are down in the face of a PC slump, but that is much worse news for Intel, given that they make worse chips.

It’s also not a fair comparison: AMD, thirteen years on from its spinout of Global Foundries, only designs chips; Intel both designs and manufactures them. It’s when you include AMD’s current manufacturing partner, TSMC, that Intel’s relative decline becomes particularly apparent:

Intel, AMD, and TSMC market caps

Of course an Intel partisan might argue that this comparison is unfair as well, because TSMC manufactures chips for a whole host of companies beyond AMD. That, though, is precisely Intel’s problem.

Intel’s Stumble

The late Clay Christensen, in his 2004 book Seeing What’s Next, predicted trouble for Intel:

Intel’s well-honed processes — which are almost unassailable competitive strengths in fights for undershot customers hungering for performance increases — might inhibit its ability to fight for customers clamoring for customized products. Its exacting manufacturing process could hamper its ability to deliver customized products. Its sales force could have difficulty adapting to a very different sales cycle. It would have to radically alter its marketing process. The VCE model predicts that operating “fast fabs” will be an attractively profitable point in the value chain in the future. The good news for IDMs such as IBM and Intel is that they own fabs. The bad news is that their fabs aren’t fast. Entrants without legacy processes could quite conceivably develop better proprietary processes that can rapidly deliver custom processors.

This sounds an awful lot like what happened over the ensuing years: one of TSMC’s big advantages is its customer service. Given the fact that the company was built as a pure play foundry it has developed processes and off-the-shelf building blocks that make it easy for partners to build custom chips. This was tremendously valuable, even if the resultant chips were slower than Intel’s.

What Christensen didn’t foresee was that Intel would lose the performance crown; rather, he assumed that performance would cease to be an important differentiator:

If history is any guide, motivated innovators will continue to do the seemingly impossible and find unanticipated ways to extend the life of Moore’s Law. Although there is much consternation that at some point Moore’s Law will run into intractable physical limits, the only thing we can predict for certain is that innovators will be motivated to figure out solutions.

But this does not address whether meeting Moore’s Law will continue to be paramount to success. Everyone always hopes for the emergence of new, unimagined applications. But the weight of history suggests the unimagined often remains just that; ultimately ever more demanding applications will stop appearing or will emerge much more slowly than anticipated. But even if new, high-end applications emerge, rocketing toward the technological frontier almost always leaves customers behind. And it is in those overshot tiers that disruptions take root.

How can we tell if customers are overshot? One signal is customers not using all of a product’s functionality. Can we see this? There are ever-growing populations of users who couldn’t care less about increases in processing power. The vast majority of consumers use their computers for word processing and e-mail. For this majority, high-end microprocessors such as Intel’s Itanium and Pentium 4 and AMD’s Athlon are clearly overkill. Windows XP runs just fine on a Pentium III microprocessor, which is roughly half as fast as the Pentium 4. This is a sign that customers may be overshot.

Obviously Christensen was wrong about a Pentium III being good enough, and not just because web pages suck; rather, the infinite malleability of software really has made it possible to not just create new kinds of applications but to also substantially rework previous analog solutions. Moreover, the need for more performance is actually accelerating with the rise of machine-learning based artificial intelligence.

Intel, despite being a chip manufacturer, understood the importance of software better than anyone. I explained in a Daily Update earlier this year about how Pat Gelsinger, then a graduate student at Stanford, convinced Intel to stick with a CISC architecture design because that gave the company a software advantage; from an oral history at the Computer Museum:

Gelsinger: We had a mutual friend that found out that we had Mr. CISC working as a student of Mr. RISC, the commercial versus the university, the old versus the new, teacher versus student. We had public debates of John and Pat. And Bear Stearns had a big investor conference, a couple thousand people in the audience, and there was a public debate of RISC versus CISC at the time, of John versus Pat.

And I start laying out the dogma of instruction set compatibility, architectural coherence, how software always becomes the determinant of any computer architecture being developed. “Software follows instruction set. Instruction set follows Moore’s Law. And unless you’re 10X better and John, you’re not 10X better, you’re lucky if you’re 2X better, Moore’s Law will just swamp you over time because architectural compatibility becomes so dominant in the adoption of any new computer platform.” And this is when x86– there was no server x86. There’s no clouds at this point in time. And John and I got into this big public debate and it was so popular.

Brock: So the claim wasn’t that the CISC could beat the RISC or keep up to what exactly but the other overwhelming factors would make it the winner in the end.

Gelsinger: Exactly. The argument was based on three fundamental tenets. One is that the gap was dramatically overstated and it wasn’t an asymptotic gap. There was a complexity gap associated with it but you’re going to make it leap up and that the CISC architecture could continue to benefit from Moore’s Law. And that Moore’s Law would continue to carry that forward based on simple ones, number of transistors to attack the CISC problems, frequency of transistors. You’ve got performance for free. And if that gap was in a reasonable frame, you know, if it’s less than 2x, hey, in a Moore’s Law’s term that’s less than a process generation. And the process generation is two years long. So how long does it take you to develop new software, porting operating systems, creating optimized compilers? If it’s less than five years you’re doing extraordinary in building new software systems. So if that gap is less than five years I’m going to crush you John because you cannot possibly establish a new architectural framework for which I’m not going to beat you just based on Moore’s Law, and the natural aggregation of the computer architecture benefits that I can bring in a compatible machine. And, of course, I was right and he was wrong.

That last sentence needs a caveat: Gelsinger was right when it came to computers and servers, but not smartphones. There performance wasn’t free, because manufacturers had to be cognizant of power consumption. More than cognizant, in fact — power usage was the overriding concern. Tony Fadell, who created the iPod and led the development of the first three generations of the iPhone, told me in an interview earlier this year:

You have to have that point of view of that every nanocoulomb is sacred and compatibility doesn’t matter, we’re going to use the best bits, but we’re not going to make sure it has to be the same look and feel. It doesn’t have to have the same principles that is designed for a laptop or a standalone desktop computer, and then bring those down to something that’s smaller form factor, and works within a certain envelope. You have to rethink all the principles. You might use the bits around, and put them together in different ways and use them differently. That’s okay. But your top concept has to be very, very different about what you’re building, why you’re building it, what you’re solving, and the needs of that new environment, which is mobile, and mobile at least for a day or longer for that battery life.

The key phrase there is “compatibility doesn’t matter”; Gelsinger’s argument for CISC over RISC rested on the idea that by the time you remade all of the software created for CISC, Intel would have long since overcome the performance delta between different architectures via its superior manufacturing, which would allow compatibility to trump the competition. Smartphones, though, provided a reason to build up the software layer from scratch, with efficiency, not performance, as the paramount goal.1

All of this still fit in Christensen’s paradigm, I would note: foundries like TSMC and Samsung could accommodate new chip designs that prioritized efficiency over performance, just as Christensen predicted. What he didn’t foresee in 2004 was just how large the smartphone market would be. While there are a host of reasons why TSMC took the performance crown from Intel over the last five years, a major factor is scale: TSMC was making so many chips that it had the money and motivation to invest in Moore’s Law.

The most important decision was shifting to extreme ultraviolet lithography at a time when Intel thought it was much too expensive and difficult to implement; TSMC, backed by Apple’s commitment to buy the best chips it could make, committed to EUV in 2014, and delivered the first EUV-derived chips in 2019 for the iPhone.

Those EUV machines are made by one company — ASML. They’re worth more than Intel too (and Intel is a customer):

Intel, AMD, TSMC, and ASML market caps

The Dutch company, to an even greater degree than TSMC, is the only lithography maker that can afford to invest in the absolute cutting edge.

From Technology to Economics

In 2021’s Internet 3.0 and the Beginning of (Tech) History, I posited that the first era of the Internet was defined by technology, i.e. figuring out what was possible. Much of this technology, including standards like TCP/IP, DNS, HTTP, etc. was developed decades ago; this era culminated in the dot com bubble.

The second era of the Internet was about economics, specifically the unprecedented scale possible in a world of zero distribution costs.

Unlike the assumptions that undergird Internet 1.0, it turned out that the Internet does not disperse economic power but in fact centralizes it. This is what undergirds Aggregation Theory: when services compete without the constraints of geography or marginal costs, dominance is achieved by controlling demand, not supply, and winners take most.

Aggregators like Google and Facebook weren’t the only winners though; the smartphone market was so large that it could sustain a duopoly of two platforms with multi-sided networks of developers, users, and OEMs (in the case of Android; Apple was both OEM and platform provider for iOS). Meanwhile, public cloud providers could provide back-end servers for companies of all types, with scale economics that not only lowered costs and increased flexibility, but which also justified far more investments in R&D that were immediately deployable by said companies.

Chip manufacturing obviously has marginal costs, but the fixed costs are so much larger that the economics are not that dissimilar to software (indeed, this is why the venture capital industry, which originated to support semiconductor startups, so seamlessly transitioned to software); today TSMC et al invest billions of dollars into a single fab that generates millions of chips for decades.

That increase in scale is why a modular value chain ultimately outcompeted Intel’s integrated approach, and it’s why TSMC’s position seems so impregnable: sure, a chip designer like MediaTek might announce a partnership with Intel to maybe produce some lower-end chips at some point in the future, but there is a reason it is not a firm commitment and not for the leading edge. TSMC, for at least the next several years, will make the best chips, and because of that will have the most money to invest in what comes next.

Scale, though, is not the end of the story. Again from Internet 3.0 and the Beginning of (Tech) History:

This is why I suspect that Internet 2.0, despite its economic logic predicated on the technology undergirding the Internet, is not the end-state…After decades of developing the Internet and realizing its economic potential, the entire world is waking up to the reality that the Internet is not simply a new medium, but a new maker of reality…

To the extent the Internet is as meaningful a shift [as the printing press] — and I think it is! — is inversely correlated to how far along we are in the transformation that will follow — which is to say we have only gotten started. And, after last week, the world is awake to the stakes; politics — not economics — will decide, and be decided by, the Internet.

Time will tell if my contention that an increasing number of nations will push back against American Internet hegemony by developing their own less efficient but independent technological capabilities is correct; one could absolutely make the case that the U.S.’s head start is so overwhelming that attempts to undo Silicon Valley centralization won’t pan out anywhere other than China, where U.S. Internet companies have been blocked for a generation.

Chips, though, are very much entering the political era.

Politics and the End-State

Taiwan President Tsai Ing-wen shared, as one does, some pictures from lunch on social media:

Taiwan President Tsai Ing-wen's Facebook post featuring TSMC founder Morris Chang

The man with glasses and the red tie in the first picture is Morris Chang, the founder of TSMC; behind him is Mark Liu, TSMC’s chairman. They were the first guests listed in President Tsai’s write-up of the lunch with House Speaker Nancy Pelosi, which begins:

台灣與美國共享的,不只有民主自由人權的價值,在經濟發展和民主供應鏈的合作上,我們也持續共同努力。

Taiwan and the United States not only share the values ​​of democracy, freedom and human rights, but also continue to work together on economic development and democratic supply chains.

That sentence captures why Taiwan looms so large, not only on the occasion of Pelosi’s visit, but to world events for years to come. Yes, the United States supports Taiwan because of democracy, freedom and human rights; the biggest reason why that support may one day entail aircraft carriers is because of chips and TSMC. I wrote two years ago in Chips and Geopolitics:

The international status of Taiwan is, as they say, complicated. So, for that matter, are U.S.-China relations. These two things can and do overlap to make entirely new, even more complicated complications.

Geography is much more straightforward:

A map of the Pacific

Taiwan, you will note, is just off the coast of China. South Korea, home to Samsung, which also makes the highest end chips, although mostly for its own use, is just as close. The United States, meanwhile, is on the other side of the Pacific Ocean. There are advanced foundries in Oregon, New Mexico, and Arizona, but they are operated by Intel, and Intel makes chips for its own integrated use cases only.

The reason this matters is because chips matter for many use cases outside of PCs and servers — Intel’s focus — which is to say that TSMC matters. Nearly every piece of equipment these days, military or otherwise, has a processor inside. Some of these don’t require particularly high performance, and can be manufactured by fabs built years ago all over the U.S. and across the world; others, though, require the most advanced processes, which means they must be manufactured in Taiwan by TSMC.

This is a big problem if you are a U.S. military planner. Your job is not to figure out if there will ever be a war between the U.S. and China, but to plan for an eventuality you hope never occurs. And in that planning the fact that TSMC’s foundries — and Samsung’s — are within easy reach of Chinese missiles is a major issue.

China, meanwhile, is investing heavily in catching up, although Semiconductor Manufacturing International Corporation (SMIC), its Shanghai-based champion, only just started manufacturing on a 14nm process, years after TSMC, Samsung, and Intel. In the long run, though, the U.S. faced a scenario where China had its own chip supplier, even as it threatened the U.S.’s chip supply chain.

This reality is why I ultimately came down in support of the CHIPS Act, which passed Congress last week. I wrote in a Daily Update:

This is why Intel’s shift to being not simply an integrated device manufacturer but also a foundry is important: yes, it’s the right thing to do for Intel’s business, but it’s also good for the West if Intel can pull it off. That, by extension, is why I’m fine with the CHIPS bill favoring Intel…AMD, Qualcomm, Nvidia, et al, are doing just fine under the current system; they are drivers and beneficiaries of TSMC’s dominance in particular. The system is working! Which, to the point above, is precisely why Intel being helped disproportionately is in fact not a flaw but a feature: the goal should be to counteract the fundamental forces pushing manufacturing to geopolitically risky regions, and Intel is the only real conduit available to do that.

Time will tell if the CHIPS Act achieves its intended goals; the final version did, as I hoped, explicitly limit investment by recipients in China, which is already leading chip makers to rethink their investments. That this is warping the chip market is, in fact, the point: the structure of technology drives inexorably towards the most economically efficient outcomes, but the ultimate end state will increasingly be a matter of politics.

I wrote a follow-up to this Article in this Daily Update.


  1. As an example of how efficiency trumped performance, the first iPhone’s processor was actually underclocked — better battery life was more of a priority than faster performance. 

Thu, 04 Aug 2022 03:51:00 -0500 Ben Thompson en-US text/html https://stratechery.com/2022/political-chips/
Killexams : IBM Expands Its Power10 Portfolio For Mission Critical Applications

It is sometimes difficult to understand the true value of IBM's Power-based CPUs and associated server platforms. And the company has written a lot about it over the past few years. Even for IT professionals that deploy and manage servers. As an industry, we have become accustomed to using x86 as a baseline for comparison. If an x86 CPU has 64 cores, that becomes what we used to measure relative value in other CPUs.

But this is a flawed way of measuring CPUs and a broken system for measuring server platforms. An x86 core is different than an Arm core which is different than a Power core. While Arm has achieved parity with x86 for some cloud-native workloads, the Power architecture is different. Multi-threading, encryption, AI enablement – many functions are designed into Power that don’t impact performance like other architectures.

I write all this as a set-up for IBM's announced expanded support for its Power10 architecture. In the following paragraphs, I will provide the details of IBM's announcement and supply some thoughts on what this could mean for enterprise IT.

What was announced

Before discussing what was announced, it is a good idea to do a quick overview of Power10.

IBM introduced the Power10 CPU architecture at the Hot Chips conference in August 2020. Moor Insights & Strategy chief analyst Patrick Moorhead wrote about it here. Power10 is developed on the opensource Power ISA. Power10 comes in two variants – 15x SMT8 cores and 30x SMT4 cores. For those familiar with x86, SMT8 (8 threads/core seems extreme, as does SMT4. But this is where the Power ISA is fundamentally different from x86. Power is a highly performant ISA, and the Power10 cores are designed for the most demanding workloads.

One last note on Power10. SMT8 is optimized for higher throughput and lower computation. SMT4 attacks the compute-intensive space with lower throughput.

IBM introduced the Power E1080 in September of 2021. Moor Insights & Strategy chief analyst Patrick Moorhead wrote about it here. The E1080 is a system designed for mission and business-critical workloads and has been strongly adopted by IBM's loyal Power customer base.

Because of this success, IBM has expanded the breadth of the Power10 portfolio and how customers consume these resources.

The big reveal in IBM’s accurate announcement is the availability of four new servers built on the Power10 architecture. These servers are designed to address customers' full range of workload needs in the enterprise datacenter.

The Power S1014 is the traditional enterprise workhorse that runs the modern business. For x86 IT folks, think of the S1014 equivalent to the two-socket workhorses that run virtualized infrastructure. One of the things that IBM points out about the S1014 is that this server was designed with lower technical requirements. This statement leads me to believe that the company is perhaps softening the barrier for the S1014 in data centers that are not traditional IBM shops. Or maybe for environments that use Power for higher-end workloads but non-Power for traditional infrastructure needs.

The Power S1022 is IBM's scale-out server. Organizations embracing cloud-native, containerized environments will find the S1022 an ideal match. Again, for the x86 crowd – think of the traditional scale-out servers that are perhaps an AMD single socket or Intel dual-socket – the S1022 would be IBM's equivalent.

Finally, the S1024 targets the data analytics space. With lots of high-performing cores and a big memory footprint – this server plays in the area where IBM has done so well.

In addition, to these platforms, IBM also introduced the Power E1050. The E1050 seems designed for big data and workloads with significant memory throughput requirements.

The E1050 is where I believe the difference in the Power architecture becomes obvious. The E1050 is where midrange starts to bump into high performance, and IBM claims 8-socket performance in this four-socket socket configuration. IBM says it can deliver performance for those running big data environments, larger data warehouses, and high-performance workloads. Maybe, more importantly, the company claims to provide considerable cost savings for workloads that generally require a significant financial investment.

One benchmark that IBM showed was the two-tier SAP Standard app benchmark. In this test, the E1050 beat an x86, 8-socket server handily, showing a 2.6x per-core performance advantage. We at Moor Insights & Strategy didn’t run the benchmark or certify it, but the company has been conservative in its disclosures, and I have no reason to dispute it.

But the performance and cost savings are not just associated with these higher-end workloads with narrow applicability. In another comparison, IBM showed the Power S1022 performs 3.6x better than its x86 equivalent for running a containerized environment in Red Hat OpenShift. When all was added up, the S1022 was shown to lower TCO by 53%.

What makes Power-based servers perform so well in SAP and OpenShift?

The value of Power is derived both from the CPU architecture and the value IBM puts into the system and server design. The company is not afraid to design and deploy enhancements it believes will deliver better performance, higher security, and greater reliability for its customers. In the case of Power10, I believe there are a few design factors that have contributed to the performance and price//performance advantages the company claims, including

  • Use Differential DIMM technology to increase memory bandwidth, allowing for better performance from memory-intensive workloads such as in-memory database environments.
  • Built-in AI inferencing engines that increase performance by up to 5x.
  • Transparent memory encryption performs this function with no performance tax (note: AMD has had this technology for years, and Intel introduced about a year ago).

These seemingly minor differences can add up to deliver significant performance benefits for workloads running in the datacenter. But some of this comes down to a very powerful (pardon the redundancy) core design. While x86 dominates the datacenter in unit share, IBM has maintained a loyal customer base because the Power CPUs are workhorses, and Power servers are performant, secure, and reliable for mission critical applications.

Consumption-based offerings

Like other server vendors, IBM sees the writing on the wall and has opened up its offerings to be consumed in a way that is most beneficial to its customers. Traditional acquisition model? Check. Pay as you go with hardware in your datacenter? Also, check. Cloud-based offerings? One more check.

While there is nothing revolutionary about what IBM is doing with how customers consume its technology, it is important to note that IBM is the only server vendor that also runs a global cloud service (IBM Cloud). This should enable the company to pass on savings to its customers while providing greater security and manageability.

Closing thoughts

I like what IBM is doing to maintain and potentially grow its market presence. The new Power10 lineup is designed to meet customers' entire range of performance and cost requirements without sacrificing any of the differentiated design and development that the company puts into its mission critical platforms.

Will this announcement move x86 IT organizations to transition to IBM? Unlikely. Nor do I believe this is IBM's goal. However, I can see how businesses concerned with performance, security, and TCO of their mission and business-critical workloads can find a strong argument for Power. And this can be the beginning of a more substantial Power presence in the datacenter.

Note: This analysis contains insights from Moor Insights & Strategy Founder and Chief Analyst, Patrick Moorhead.

Moor Insights & Strategy, like all research and tech industry analyst firms, provides or has provided paid services to technology companies. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, and speaking sponsorships. The company has had or currently has paid business relationships with 8×8, Accenture, A10 Networks, Advanced Micro Devices, Amazon, Amazon Web Services, Ambient Scientific, Anuta Networks, Applied Brain Research, Applied Micro, Apstra, Arm, Aruba Networks (now HPE), Atom Computing, AT&T, Aura, Automation Anywhere, AWS, A-10 Strategies, Bitfusion, Blaize, Box, Broadcom, C3.AI, Calix, Campfire, Cisco Systems, Clear Software, Cloudera, Clumio, Cognitive Systems, CompuCom, Cradlepoint, CyberArk, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Dialogue Group, Digital Optics, Dreamium Labs, D-Wave, Echelon, Ericsson, Extreme Networks, Five9, Flex, Foundries.io, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Revolve (now Google), Google Cloud, Graphcore, Groq, Hiregenics, Hotwire Global, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, IBM, Infinidat, Infosys, Inseego, IonQ, IonVR, Inseego, Infosys, Infiot, Intel, Interdigital, Jabil Circuit, Keysight, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, Lightbits Labs, LogicMonitor, Luminar, MapBox, Marvell Technology, Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco), Merck KGaA, Mesophere, Micron Technology, Microsoft, MiTEL, Mojo Networks, MongoDB, MulteFire Alliance, National Instruments, Neat, NetApp, Nightwatch, NOKIA (Alcatel-Lucent), Nortek, Novumind, NVIDIA, Nutanix, Nuvia (now Qualcomm), onsemi, ONUG, OpenStack Foundation, Oracle, Palo Alto Networks, Panasas, Peraso, Pexip, Pixelworks, Plume Design, PlusAI, Poly (formerly Plantronics), Portworx, Pure Storage, Qualcomm, Quantinuum, Rackspace, Rambus, Rayvolt E-Bikes, Red Hat, Renesas, Residio, Samsung Electronics, Samsung Semi, SAP, SAS, Scale Computing, Schneider Electric, SiFive, Silver Peak (now Aruba-HPE), SkyWorks, SONY Optical Storage, Splunk, Springpath (now Cisco), Spirent, Splunk, Sprint (now T-Mobile), Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, Telesign,TE Connectivity, TensTorrent, Tobii Technology, Teradata,T-Mobile, Treasure Data, Twitter, Unity Technologies, UiPath, Verizon Communications, VAST Data, Ventana Micro Systems, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zayo, Zebra, Zededa, Zendesk, Zoho, Zoom, and Zscaler. Moor Insights & Strategy founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Dreamium Labs, Groq, Luminar Technologies, MemryX, and Movandi.

Wed, 13 Jul 2022 12:00:00 -0500 Matt Kimball en text/html https://www.forbes.com/sites/moorinsights/2022/07/14/ibm-expands-its-power10-portfolio-for-mission-critical-applications/
Killexams : Online Bachelor's Degree in Software Engineering No result found, try new keyword!Students may demonstrate proficiency in certain programming languages, such as Java or C++, by earning certification in those languages. Companies such as Microsoft, Oracle and IBM offer ... Fri, 26 Apr 2019 03:16:00 -0500 text/html https://www.usnews.com/education/online-education/software-engineering-bachelors-degree Killexams : How to be an AI & ML expert: A Webinar with Cloud Architect Subhendu Dey

Summary

The webinar outlined what AI and ML mean in today’s world and how students could get involved

Mr Subhendu Dey also laid out a comprehensive roadmap for those looking to start a career in AI and ML

Artificial Intelligence and Machine Learning as disciplines have taken the world by storm, particularly in the 21st century. While many youngsters have drawn inspiration from some of the best science fiction featuring AI and robots, the real world of AI and ML has been growing by leaps and bounds. But what does the world of AI and ML have to offer? How can you transition from campus to career with AL & ML? And how can you be an expert in AI & ML? To answer these and many other questions, The Telegraph Online Edugraph organised a webinar with Subhendu Dey, a Cloud Architect and advisor on Data and AI.

The webinar saw participants from class 8 right up to those in advanced degrees, as well as teachers. Hence, the subject matter of the webinar contained takeaways that would be relevant at all stages. Mr Dey also highlighted that he would be focusing on showing how things that have always existed around us contribute to AI - giving students a more intuitive idea of AI and making it more interesting.

The webinar started by taking a look at a simple action like sending a text. People would find that their mobiles would keep suggesting words to them. Be it as soon as they have typed a few letters or after they have typed a few words, they would get suggestions that are surprisingly accurate. This is called Language Modelling and requires an intuitive understanding of language. A human may be able to do it from his or her extensive knowledge of words and language, but in this case, it is a fine demonstration of the intuitiveness of AI.

Let’s look at another aspect of AI - when we key in a question into the Google search bar, a decade or so ago, Google would have analysed the keywords and thrown up a list of links that feature the keywords. But fast-forward to this decade and Natural Language search is today capable of not just reading the keywords but also finding out the intent behind the query. This means that Google will, in addition to giving you the links, also supply you the answer, as well as other questions that have the same or related intent. In fact, Google also has a system for taking feedback, which facilitates the Google AI to learn to be even more intuitive and better at giving suggestions.

One need only look at the digital assistant - Siri, Google Assistant or Alexa - to understand the advancements in AI. From understanding spoken queries to giving intuitive, and often very witty, answers, these assistants communicate in a surprisingly human-like manner. Of course, there is a cycle of tasks that they must perform behind the scenes, which Mr Dey spoke about in detail.

While these changes that we can observe are new, AI has been around for a long time now. One of the earliest feats was in 1997, when the IBM Supercomputer Deep Blue beat world chess champion Gary Kasparov, in a six-match tournament.

Today artificial intelligence is a booming area of development and the Ministry of Electronics & Information Technology projects the addition of about 20 million jobs in the sector by 2025. In fact, this is also underscored by multiple studies and reports prepared by global auditing firms like Deloitte, NASSCOM and PwC.

However, one question that has always baffled scientists and engineers working in the domain of AI, is striking a balance between behaviour and reasoning on the one hand and human/irrational and rational on the other, when designing the various Artificial Intelligence agents. It has, however, been found that more intuitive AI agents with better user experience interfaces have a higher penetration in human society.

Next we take a look at Machine Learning. When an AI agent learns on its own from the interactions it has, this is known as Machine Learning. When humans learn something, it registers in some form in the mind. However, machines perceive data in the form of functions and variables. With Machine Learning, AI agents create models which exist as executable software components made up of a sequence of mathematical variables and functions. Hence, becoming an expert in AI and ML usually requires a person to have a sound understanding of mathematics and statistics.

Speaking of building a career in AI and ML, Mr Dey threw light on three avenues into the industry. These are:

  • As a scientist
  • As an engineers
  • As a contributor

Let’s take a look at each of these.

As a Scientist

As mentioned above, to communicate with AI, your query must be represented in a mathematical/logical format. Hence, when choosing your educational degrees or courses, go for courses that cover the following Topics which contribute to the core of AI:

  • Vectors and Matrices
  • Probability
  • Relation and Function
  • Differential Calculus
  • Statistical Analysis

Choosing a major which covers these aspects should arm you with the knowledge and skills you need to become a scientist in AI.

As an Engineer

Being a scientist is not your only option, though. AI also depends heavily on engineers to grow and develop. From the engineering perspective, here is a list of functions that need to be carried out:

  • Visualisation/representation of data
  • Collection of data from multiple sources
  • Building pipelines to prepare data to scale
  • Using Machine Learning services/frameworks available on clouds to scale up
  • Test, audit and explain to various stakeholders the Machine Learning output

As a contributor

If you find you are not interested in being a scientist of an engineer, there are other significant ways you can contribute to AI. That could be in the following areas:

  • User experience design
  • Process modelling
  • Domain knowledge
  • Linguistic details
  • Social aspects

Mr Dey discusses all these avenues at length in the course of the webinar with examples. At the same time, he lays out the basic qualities that one must have - irrespective of which role one chooses to pursue. And these are creative vision, innate curiosity and perseverance.

Here are some courses that you should explore if you want to build a career in the core AI aspects:

  • A Bachelors or Masters degree in Computer Science or Engineering or Mathematics or Statistics.
  • A specialisation in any of the following areas:
    • Artificial Intelligence
    • Machine Learning
    • Data Science
    • Automation and Robotics
  • B Tech/ BE in other engineering fields, followed by work experience in the field of software or IT.
  • Artificial Intelligence
  • Machine Learning
  • Data Science
  • Automation and Robotics

The webinar ended with a detailed Q&A session which opened with some questions received from participants submitted at the time of registration and carried on to questions asked by participants in the course of the webinar. The Q&A covered a range of interesting Topics like:

  • Neural networks/deep learning
  • Importance of Maths and Statistics in AI/ML
  • How valuable are practical projects for developing skills needed to work in AI/ML
  • Which programming language is the best to learn for a career working with AI/ML
  • Which are the best courses to consider as a student - traditional degrees or online certification courses
  • How does AI compare to the human brain
  • Will AI and automation endanger human jobs in the future
  • What are intelligent agents and how are they useful in AI

To learn the answer to these and many more questions, watch our video recording of the live webinar.

A career in AI and ML is an excellent choice now - and this small initiative of The Telegraph Edugraph was aimed at providing the right guidance for you to make the transition from Campus to Career. Best of luck!

Last updated on 26 Jul 2022

Mon, 25 Jul 2022 01:42:00 -0500 text/html https://www.telegraphindia.com/edugraph/career/how-to-be-an-ai-ml-expert-a-webinar-with-cloud-architect-subhendu-dey/cid/1876427
Killexams : Academic Excellence and Creative Brilliance - Manav Rachna is a name to reckon with Manav Rachna Educational Institutions have made their name in helping students advance in their careers. Factors like robust infrastructure, experienced faculty, well-managed and well-planned academic structure, great campus life, and placement record help an aspirant make an informed decision while choosing an institution for higher studies. Besides this, universities like Manav Rachna extensively focus on knowledge, employability skills, specific technical competencies, ability to articulate, and achievements.

Manav Rachna offers 100+ undergraduate and postgraduate courses and has made its mark with NAAC ‘A’ Grade Accreditation. The curriculum is designed for blended learning. Students can make the most out of the corporate bridge that shapes them into industry-ready professionals.


The institution has received a 5 Star and diamond rating from the QS Rating System for its teaching, employability, academic pursuits, amenities, community involvement, and inclusivity.

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You have successfully cast your vote


MREI offers top-notch education in various sectors and is a recognizable symbol of expertise and knowledge. Over the past 25 years, the institutions have produced over 34,000+ alumni, 600+ reputable multinational enterprises and Indian corporations that support MREI, 535+ patents (filed/granted), 7800+ research papers published in international and national journals and conferences, 60+ international academic alliances, and 80+ Alumni & In Campus startups.
In order to satisfy the changing needs of the global workforce and guarantee that industry requirements are met, the institution regularly improves its educational offerings. All of the university's courses are continuously evaluated by accrediting organizations. They are all strengthened by the invaluable knowledge gained from relationships with businesses and affiliations with national & international agencies.


Academic excellence blended with corporate exposure

It is recognized by various government and private ranking and accreditation bodies. Manav Rachna International Institute of Research and Studies (MRIIRS) and Manav Rachna University (MRU) are founder members of the prestigious “College Board’s Indian Global Higher Education Alliance''.

Collaborations with eminent industry partners create an encouraging environment for global career development, corporate exposure and opportunities, along with preparing the students to gain market skills so that they can future-proof their careers in the ever-changing industry scenario and trends.

Manav Rachna students get to tap into experiential learning with industry-integrated programmes in collaboration with industry knowledge partners. These include VLSI Design and verification with TrueChip, Digital Banking and Financial Markets with Bombay Stock Exchange, Hospitality and Hotel Administration with IHG - The Crowne Plaza® and Networking, Cyber Security, Programming, IoT, Programmable Infrastructure and Linux, and so much more.

Centre of Excellence for Electric Vehicle, Centre of Excellence for Culinary Art, Centre for Peace and Sustainability and Centre of Excellence for Product Design & Development provide students with hands-on experience in their areas of interest.

Manav Rachna has tie-ups with global Universities from Australia, New Zealand, UK and USA for International Pathway Programs. Priyanka Walter, 7th Semester, B.A. LL.B (H), School of Law, completed her Summer School at the London School of Economics and Political Science, UK.

Manav Rachna's placement and training cell enable students to take up internship and placement opportunities in established companies in India with the highest package of 45 LPA. Some top companies visiting for campus placements include Yamaha, Zomato, IBM, Indigo, Cognizant, KPMG, LinkedIn, HCL, Lido Learning, Accenture etc.

Manav Rachna's corporate relations and career management centre also helps students with soft skills to brush up on their interpersonal communication, team building and leadership qualities that enhance their employability. Innovation sits at the heart of Manav Rachna Educational Institutions. These offer New Generation Innovation and Entrepreneurship Development Centre (New Gen IEDC) to support the students' entrepreneurial dreams.

Placement Opportunities

Manav Rachna Educational Institutions (MREI) continually works to strengthen the connection between academic and business communities. The placement process for internship opportunities and final careers are crucial components of each university's yearly schedule of events. MREI provides a well-organized, methodical approach to addressing corporate requirements and students' professional ambitions. The placement department links the University's academic departments, business partners, and students. The placement operations are done by and with the participation of students, and the procedure is open and transparent.

This department at Manav Rachna welcomes top companies from every industry to its campus, where students are assisted through the entire selection procedure. MREI has been associated with over 600 recruitment partners, many of which are listed as Fortune 500 companies. Accenture, Tata Motors, Amazon, Meritnation, IndiGo, Capgemini, HCL, IBM, Hindustan Times, Ericsson India Pvt. Ltd., Crowne Plaza, Deloitte, Cognizant, Hero Honda, Infosys, and several other businesses are just a few names amongst the larger giants who visit the campus for placement.

In the session 2021-22, students of MREI have made their mark with 850+ placement offers from 200+ reputed companies.

In order to assess the possibilities of providing opportunities for career development for students, a fully operational placement cell has been established by MREI. that communicates and engages with the business world regularly. Manav Rachna is a community that cherishes cultural variety, compassion for age, gender, and socio-economic background.

Internships and projects are mandatory for most courses offered at Manav Rachna. For example, a one-week Industry Interaction program at IBM in Bangalore and access to its online Eco-system Platform - Innovation Center for Open Standards help CSE students gauge new industry trends. A compulsory internship for six months during the fourth year has been included in the curriculum of Bachelor of Architecture. Engineering, BBA, Nutrition and Dietetics, Applied Sciences, Physiotherapy, and MBA students are encouraged to undertake internships, live projects and consultancy work.

Career Development Centre at Manav Rachna imparts pre-placement, soft skills and employability training. The 2022 batch of students pursuing various courses at Manav Rachna’s universities has already been placed in leading companies. Yamaha, Amazon, IDFC Bank, Qi Spine, IBI Group etc., have offered lucrative packages to engineering and non-engineering students of 2022.

Recently placed with LinkedIn at an annual package of INR 23 Lakhs, Vinayak, student of Computer Science and Engineering (Batch: 2015-19), shares: “My four years at Manav Rachna University have been excellent and a memory to cherish for a lifetime. The years spent here have been full of learning opportunities coupled with the academic grind that one has to go through. The most important thing of this institution is the caring and supportive nature of faculty members. My sincere gratitude and appreciation to all the faculty members who always supported me and made sure that I can be what I am today.”

Aashlesha Sharma (B. Tech – Mechanical Engineering) at Manav Rachna interned as a Technical Student at CERN, the world's largest Nuclear and Particle Physics Laboratory situated in Geneva, Switzerland, on a monthly stipend of 3283 CHF (~ 2,24,130.05 INR).

Dr Harshita Joshi, BDS Programme, MRDC, ranked fourth in AIIMS Entrance exam and secured 21st rank in the All India NEET Entrance Exam.

Sahil Jhangar, Student of B.Tech CSE 2019-23 Batch, MRU, has been selected for the GSoC (Google Summer of Code) 2022 program for a 4-month project with a stipend of $3000.


Scholarships worth 8 Crore availed by students in 2021
It has become tough for academic institutions to determine a student's ability for enrolment due to shifting educational trends. The online test format has evolved into the new baseline that the learners have used in accurate years. Keeping this in mind, MRNAT, Manav Rachna National Aptitude Test, is a prerequisite for entrance to MREI. For the prospective applicants for admission, JEE, CAT, XAT, MAT, CLAT, NID, ATMA and other competitive exam scores are also given consideration. Scholarships are offered to students seeking admission in Undergraduate and Postgraduate Courses through these competitive exams.
For more details, click here.

Disclaimer: This article has been produced on behalf of Manav Rachna University by Times Internet’s Spotlight team.

Thu, 21 Jul 2022 17:36:00 -0500 text/html https://timesofindia.indiatimes.com/spotlight/academic-excellence-and-creative-brilliance-manav-rachna-is-a-name-to-reckon-with/articleshow/93044639.cms
Killexams : Serverless Architecture Industry Size Worth $21,988.07 Billion By 2025 | CAGR: 27.8%, Allied Market Research

(MENAFN- EIN Presswire)

Serverless Architecture Industry

Several benefits such as enhanced scalability and cost-efficiency of serverless architecture propel the growth of the market.

PORTLAND , PORTLAND, OR, UNITED STATE, July 17, 2022 /EINPresswire.com / -- Rapid rise of the app development market along with increase in demand for useful applications for different platforms such as Android and iOS have boosted the growth of the serverless architecture Industry .

Growing shift from DevOps to serverless computing and rising need to eliminate server management challenges have supplemented to the growth of the market. The region of North America region accounted for nearly half of the market of global serverless architecture in terms of revenue in 2017.

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According to the report published by Allied Market Research, the global serverless architecture industry generated $3.01 billion in 2017, and is estimated to reach $21.99 Billion by 2025, growing at a CAGR of 27.8% from 2018 to 2025. The report offers a detailed analysis of the key segments, top investment pockets, changing dynamics, market size & estimations, and competitive scenario.

The public cloud segment accounted for nearly three-fourths of the total market share in 2017, and is expected to maintain its dominance by 2025. The major factors that drive the growth of this segment include its high availability, cost-efficiency and capabilities to Improve the functionality as well as overall development process. However, the private cloud segment is estimated to project the fastest CAGR of 30.0% from 2018 to 2025, owing to its less vendor-locking problems and enhanced security.

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Based on applications, the web application development segment held nearly half of the total market share in 2017, and will maintain its dominance throughout the forecast period. This is due to serverless computing that allows developing and running of an application without the servers. This reduces the complex procedures such as planning capacity of the application, installation of hardware, procurement, and software.

However, the IoT backend segment is estimated to register the highest growth rate with a CAGR of 31.7% from 2018 to 2025, owing to growing IoT industry and increasing number of data sets associated with these connected devices.

Asia-Pacific would grow at the fastest CAGR of 31.0% from 2018 to 2025, owing to factors such as on-going IT modernization in well-established telecommunication industries and increase in adoption of IoT-based devices which are expected to provide remunerative opportunities for market.

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However, North America contributed to nearly half of the total share in 2017 and is estimated to maintain its dominance during the forecast period. This is due to growth of the app development market, well-established cloud industry, and significant adoption of serverless architecture for media processing and IoT applications.

Leading market players analyzed in the research include the Amazon Web Services, Alibaba Group, Google LLC, Oracle Corporation, Microsoft Corporation, IBM Corporation, Platform9 Systems, Inc., Twilio, Rackspace Inc., and Tibco Software.

Key Benefits for Serverless Architecture Market:

•This study includes the analytical depiction of the global serverless architecture market trends and future estimations to determine the imminent investment pockets.

•The report presents information related to key drivers, restraints, and opportunities.

•The current serverless architecture market is quantitatively analyzed from 2017 to 2025 to highlight the financial competency of the industry.

•Porter's five forces analysis illustrates the potency of buyers & suppliers in the serverless architecture industry.

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Sun, 17 Jul 2022 16:37:00 -0500 Date text/html https://menafn.com/1104544189/Serverless-Architecture-Industry-Size-Worth-2198807-Billion-By-2025-CAGR-278-Allied-Market-Research
Killexams : Mythic: How An Analog Processor Could Revolutionize Edge AI

Several companies, from IBM to RAIN Neuromorphic, see the potential, but Mythic is first to market.

Alberto Romero, Cambrian-AI Analyst, contributed to this article.

Mythic is an analog AI processor company conceived to overcome the growing limitations of digital processors. Founded by Mike Henry and Dave Fick, and based in Texas, Austin, and Redwood City, California, Mythic aims to solve the technical and physical bottlenecks that limit current processors through the use of analog compute in a world dominated by digital technology. Mythic wants to prove that, contrary to common belief, analog isn’t a relic of the past, but a promise for the future.

Two main problems inhibit the pace of development of digital hardware: The end of Moore’s Law and the Von Neumann architecture. For 60 years we’ve enjoyed ever-increasing powerful hardware as predicted by Gordon Moore in 1965, but as we approach the minimum theoretical size of transistors, his well-harnessed law seems to be coming to an end. Another well-known issue is the need in the Von Neumann architecture to move data from memory to the processor and back to make the computations. This approach is increasingly being replaced by compute-in-memory (CIM) or compute-near-memory approaches that significantly reduce memory bandwidth and latency while increasing performance.

The comeback of analog compute?

Mythic claims it has built a unique, paradigm-shifting solution that promises to tackle digital’s limitations while providing improved specifications compared to the best-in-class digital solutions: an analog compute engine (ACE). Historically, analog computers were replaced by digital due to the latter’s reduced cost and size and their general-purpose nature. However, the current landscape of AI is dominated by deep neural networks (DNNs) which don’t require extreme precision and, more importantly, the majority of the computing bulk goes into a single operation: matrix multiplication. The perfect opportunity for analog compute.

On top of it, Mythic is exploiting the advantages of CIM and dataflow architecture to obtain impressive early results. They’ve taken CIM to the extreme by computing directly in the flash memory cells. Their analog matrix processors take the inputs as voltage, the weights are stored as resistance, and the output is the resulting current. In addition, the dataflow design keeps these processes running in parallel, which allows for extremely fast and efficient calculations while maintaining high performance. A clever combination of analog computing, CIM, and dataflow architecture defines the Mythic ACE, the company’s main differentiating technology.

Mythic’s ACE meets the requisites of edge AI inference

Mythic’s tech promises high performance at very low power, ultra-low latency, low cost, and small form factor. The basic element is their Analog Matrix Processor (AMP) which features an array of tiles, each containing the ACE complemented by digital elements: SRAM, a vector SIMD unit, a NoC router, and a 32-bit RISC-V nano processor. The innovative design of the ACE eliminates the need for DDR DRAM, reducing latency, cost, and power consumption. AMP chips can be scaled, providing support for large or multiple models. Their first product, the single-chip M1076 AMP (76 AMP tiles) can handle many endpoint applications and can be scaled up to 4-AMPs or even 16-AMPs on a single PCI express card, adequate for edge server-level high-performance usage.

The hardware is complemented with a software stack that provides a seamless pipeline going from the graph (ONNX and PyTorch) to an AMP-ready package through a process of optimization (including a quantization to analog INT8) and compilation. Mythic’s platform also supports a library of ready-to-go DNNs, including object detection/classification (YOLO, ResNet, etc.) and pose estimation models (OpenPose).

The company’s full-stack solution leverages the potential of analog processors while maintaining relevant features of the digital world. It makes the M1076 AMP a great option to handle AI workloads for inference at the edge faster and more efficiently — the company claims it provides the “best-in-class TOPS/W” — than its fully-digital counterparts. That, and the company’s broad offering of products and AI models, make it well-posited to target fast-growing edge AI-focused markets like video surveillance, smart home devices, AR/VR, drones, and robotics.

So far, it seems Mythic has transformed an innovative idea into promising tech to compete for edge inference AI. Now, let’s see the numbers. The company claims the M1076 AMP performs at up to 25 TOPS running at around 3W. Compared to similar digital hardware, that’s a reduction in power consumption of up to 10x. And it can store up to 80M weights on-chip. The MP10304 Quad-AMP PCIe card can deliver up to 100 TOPS at 25W and store 320M weights. When we compare these claims to those of many others, we can’t help but be impressed.

Conclusions

The success of analog AI will depend on achieving high density, high throughput, low latency, and high energy efficiency, while simultaneously delivering accurate predictions. Compared to pure digital implementations analog circuits are inherently noisy, but despite this challenge, the benefits of analog compute become apparent as processors like the M1076 are able to run larger DNN models that feature higher accuracy, higher resolution or lower latency.

As Mythic continues to refine its hardware and software, we will look forward to seeing benchmarks that can demonstrate the platform’s capabilities and power efficiency. But we have seen enough already to be excited by the potential of this unique approach.

Sat, 30 Jul 2022 06:20:00 -0500 Karl Freund en text/html https://www.forbes.com/sites/karlfreund/2022/07/30/mythic-how-an-analog-processor-could-revolutionize-edge-ai/
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