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Rapid improvements in fast information storage capacity, high computing power, and parallelization have contributed to the swift uptake of the deep learning technology in end-use industries such as automotive and healthcare. Further, the need for understanding and analyzing visual contents among enterprises in order to gain meaningful insights, is expected to provide traction to the industry over the forecast period. The increasing prominence of Graphics Processing Unit (GPU)-accelerated applications is leading to increased adoption of the technology in scientific disciplines such as deep learning and data science. Organizations are utilizing deep learning neural networks to extract valuable insights from enormous amounts of data for providing innovative products and improving customer experience; thereby, increasing revenue opportunities. The technology is expected to gain prominence among key players and researchers, owing to its use in improvising artificial intelligence capabilities in Natural Language Processing (NLP), image & speech recognition, and computer vision areas. Solution providers are resorting to partnerships and collaborations to enter the deep learning space. For instance, in January 2016, Movidius collaborated with Google, Inc. to enhance its deep learning capabilities on mobile devices. In September 2016, Intel Corporation announced the acquisition of Movidius for improvising its computer vision and deep learning solutions. Browse full research report with TOC on "Deep Learning Market Analysis By Solution, By Hardware (CPU, GPU, FPGA, ASIC), By Service, By Application (Image Recognition, Voice Recognition, Video Surveillance), By End-use, By Region, And Segment Forecasts, 2014 - 2025" at: http://www.grandviewresearch.com/industry-analysis/deep-learning-market Further key findings from the report suggest: Grand View Research has segmented the deep learning market based on solutions, hardware, services, applications, end-uses, and regions: Grand View Research, Inc. is a U.S. based market research and consulting company, registered in the State of California and headquartered in San Francisco. The company provides syndicated research reports, customized research reports, and consulting services. To help clients make informed business decisions, we offer market intelligence studies ensuring relevant and fact-based research across a range of industries, from technology to chemicals, materials and healthcare.


Rapid improvements in fast information storage capacity, high computing power, and parallelization have contributed to the swift uptake of the deep learning technology in end-use industries such as automotive and healthcare. Further, the need for understanding and analyzing visual contents among enterprises in order to gain meaningful insights, is expected to provide traction to the industry over the forecast period. The increasing prominence of Graphics Processing Unit (GPU)-accelerated applications is leading to increased adoption of the technology in scientific disciplines such as deep learning and data science. Organizations are utilizing deep learning neural networks to extract valuable insights from enormous amounts of data for providing innovative products and improving customer experience; thereby, increasing revenue opportunities. The technology is expected to gain prominence among key players and researchers, owing to its use in improvising artificial intelligence capabilities in Natural Language Processing (NLP), image & speech recognition, and computer vision areas. Solution providers are resorting to partnerships and collaborations to enter the deep learning space. For instance, in January 2016, Movidius collaborated with Google, Inc. to enhance its deep learning capabilities on mobile devices. In September 2016, Intel Corporation announced the acquisition of Movidius for improvising its computer vision and deep learning solutions. Browse full research report with TOC on "Deep Learning Market Analysis By Solution, By Hardware (CPU, GPU, FPGA, ASIC), By Service, By Application (Image Recognition, Voice Recognition, Video Surveillance), By End-use, By Region, And Segment Forecasts, 2014 - 2025" at: http://www.grandviewresearch.com/industry-analysis/deep-learning-market Further key findings from the report suggest: Grand View Research has segmented the deep learning market based on solutions, hardware, services, applications, end-uses, and regions: Grand View Research, Inc. is a U.S. based market research and consulting company, registered in the State of California and headquartered in San Francisco. The company provides syndicated research reports, customized research reports, and consulting services. To help clients make informed business decisions, we offer market intelligence studies ensuring relevant and fact-based research across a range of industries, from technology to chemicals, materials and healthcare.


CAMBRIDGE, United Kingdom, 26th May 2017 - UltraSoC today announces the completion of a £5m ($6.4m) funding round to drive continued deployment of its technology and realize its vision of embedding intelligent analytics capabilities into every chip. Atlante Tech leads a strong line up of new investors including Enso Ventures, Oxford Capital, and successful CEO and serial entrepreneur Guillaume d'Eyssautier, who join existing investors Octopus Ventures and South East Seed Fund (FSE Group). “Hard tech is back in favor with the UK and global investment community, with recent funding for Ultrahaptics, Graphcore and SiFive (a fellow RISC-V proponent), plus successful exits at Movidius and Mobileye,” said Rupert Baines, UltraSoC CEO. “Our investors are excited by the potential of UltraSoC’s technology and are committed to supporting our aim of putting intelligent analytics into every chip.” As part of the funding, Miles Kirby of Oxford, Kirill Mudryy of Enso and Alvise Bonivento of Atlante will join the UltraSoC board, alongside existing investor representative Luke Hakes (Octopus), board Chair Chris Gilbert and non-executive director Chris Wade. UltraSoC’s semiconductor intellectual property (SIP) enables designers to easily and cost-effectively create complex SoCs (systems on chip) with built-in intelligence that continuously monitors and responds to real-world behavior. This allows SoCs to optimize power consumption and performance and deal with security threats or safety breaches. Successful development of the company’s debug tools and increased awareness of the technology’s potential benefits has meant a series of significant commercial engagements, with more in the pipeline. Amongst others, HiSilicon (Huawei), Imagination Technologies, Movidius (now Intel), and Microsemi all use UltraSoC technology in their designs, delivering proven hardware-embedded benefits to their customers. To ensure these benefits are accessible to customers in all sectors across the globe, UltraSoC partners with leading names in the semiconductor industry including ARM, Baysand, Cadence/Tensilica, CEVA, Codasip, Lauterbach, MIPS and Teledyne LeCroy. The significant line-up of new and existing investors in this funding round reflects the company’s growing commercial traction and technological progress. Likewise, the company’s potential is being realized as the need for safety, security and performance-tuning becomes critical. Embedded analytics allows the chip to monitor and optimize its own behavior at a hardware level; and provides insights that enable engineers to make improvements and fix problems. The same technology can detect evolving real-world threats and problems – for instance those caused by malicious attacks. These features benefit any electronic system, but are particularly attractive in the automotive and high-performance computing (HPC) sectors. The investment in UltraSoC also reflects changes taking place in market areas where embedded analytics offers chip makers and their customers a distinct competitive advantage. Dr. Alvise Bonivento of lead investor Atlante Tech commented on this point: “UltraSoC has a great team, technology and substantial commercial traction. The time is right for UltraSoC’s embedded intelligence to make a significant difference to mass market and mission critical applications.” UltraSoC chairman, Chris Gilbert, commented: “We are delighted to receive the backing of prominent industry figures and distinguished investors like Atlante, Enso and Oxford, as we take UltraSoC to the next level of success. It’s great to welcome these new investors on board, and we are delighted to retain the support of our existing backers Octopus Ventures and the FSE Group’s South East Seed Fund.” Kirill Mudryy, Partner at Enso Ventures, added: “We at Enso Ventures are always looking to support companies that have innovative and truly disruptive technology. That is why we are delighted to add UltraSoC to our portfolio.” UltraSoC was named one of the 100 most exciting companies in the UK in the 2016 Mishcon de Reya CityAM “Leap 100” list, and nominated by Gartner as one of its 2016 “Cool Vendors”. It was recognized as “Best New Company” in the 2015 ELEKTRA Awards. UltraSoC’s flagship product line is a suite of semiconductor IP that allows chip designers to integrate an intelligent analytics infrastructure into the core hardware of their devices. By monitoring and analyzing the real-world behavior of entire systems via UltraSoC’s intelligent analytics embedded in the silicon, engineers can take action to reduce system power consumption, increase performance, protect against malicious intrusions, and ensure product safety. These capabilities address applications in a broad range of market sectors, from automotive and IoT products, to at-scale computing and communications infrastructure. Silverpeak, the technology investment bank, acted as exclusive financial advisor to UltraSoC in the transaction. About UltraSoC UltraSoC is an independent provider of SoC infrastructure that enables rapid development of embedded systems based on advanced SoC devices. The company is headquartered in Cambridge, United Kingdom. For more information visit www.ultrasoc.com About Atlante Tech Atlante Tech is a new fund promoted by IMI Fondi Chiusi Sgr SpA dedicated to investing in high tech start-ups with high growth potential especially in electronics, embedded systems, big data, medical technologies, and cleantech. Atlante Tech relies on the experience of the other funds of the Atlante family (Atlante Ventures, Atlante Seed, Atlante Ventures Mezzogiorno) that are among the most active Italian VC investors both in terms of number of investments and in terms of exits and IPOs. Atlante Tech has recently reached its first closing at 30M € (subscribed by Intesa Sanpaolo group) and is currently raising other capital on the market with the goal of reaching 120M€. About Enso Ventures Enso Ventures is a venture capital firm that specializes in making selective investments in groundbreaking, high-technology and bio-tech companies. Enso Ventures provides the required capital along with leadership and industry expertise with a clear focus on technology acceleration and commercial development to promote faster growth. The focus is on disruptive technology platforms in high growth market segments, products addressing unmet global market needs with the potential to become market leaders. Enso Ventures has offices in London and New York. About Octopus Ventures Octopus Ventures is a London and New York based venture capital firm, focused on identifying unusually talented entrepreneurs. In recent years we have been fortunate to back the founding teams of over 60 companies, including Conversocial, graze.com, LoveFiLM, Property Partner, Secret Escapes, Sofar Sounds, Swiftkey, Swoon Editions, Uniplaces, tails.com, Zoopla Property Group and Zynstra. We can invest from £250,000 to £25 million in a first round of funding and will look to follow in subsequent rounds. We are proud to be known as one of the most entrepreneur friendly investors in Europe and a significant part of our portfolio consists of referrals from teams we have already invested in or serial entrepreneurs who we have previously backed. Octopus Ventures is part of the Octopus group. Octopus is a fast-growing UK fund management business with leading positions in several specialist sectors including property finance, healthcare, energy and smaller company investing. Founded in 2000, Octopus manages more than £6 billion of funds on behalf of 50,000 investors. www.octopusventures.com About Oxford Capital Oxford Capital has been making venture capital investments since 1999. Our London-based ventures team has more than 70 years of combined experience, gained at leading investors including Qualcomm, HG Capital, DN Capital, Draper Esprit and Arma Partners. We invest in early-stage businesses and we want to back entrepreneurs who are trying to solve big problems in innovative ways. We aim to invest in sectors where the UK has the potential to lead the world. Current areas of interest include Software-as-a-Service, marketplaces, mobility, gaming, fintech, digital health, machine learning and artificial intelligence www.oxcp.com About The South East Seed Fund The South East Seed Fund is managed by The FSE Group, a regional funding organisation, backed by BIS and privately governed. With access to a breadth of public and private resources, FSE specialises in the identification, funding and development of ambitious businesses with high growth potential. By providing a unique funding service to these companies across the South East, FSE helps them grow through direct investment. The South East Seed Fund has directly invested £5m in SE companies and has helped them raise a further £100m http://www.thefsegroup.com/south-east-seed-fund/


Research and Markets has announced the addition of the "Virtual Reality for Consumer Markets" report to their offering. Industry players continue fine-tuning their products so as not to muddy the water for all involved. It is believed that these efforts will bear fruit in the coming years, and that combined revenue for head-mounted displays (HMDs), VR accessories, and VR content will increase from $453.6 million in 2015 to $35.0 billion worldwide in 2021, representing a compound annual growth rate (CAGR) of 133%. The year 2016 will be remembered as the debut of consumer virtual reality (VR), with key ambassadors in the form of Facebook/Oculus, HTC/Valve, Sony, Samsung, and a collective community of companies in China planting their stakes in the ground with formidable investments in jumpstarting a new computing platform. After a shaky start, Facebook's Oculus Rift and HTC/Valve's VIVE started selling in the U.S. in 3Q 2016 and are stabilizing their ecosystems and distribution in 4Q 2016, as they are joined by Sony with the debut of PlayStation VR. A number of lessons have been learned since the 1990s when consumer VR last generated this much hype, with huge strides having been made in terms of creating compelling content and a convincing level of immersion. Getting users to experience VR technology firsthand, and therefore truly understand its potential, remains a challenge, but the emergence of low-cost mobile VR solutions is helping. Even so, some industry participants strongly believe that anything requiring the user to wear a cumbersome device will ultimately fail. The stakes are high given the huge amount of money invested in the industry by some of the world's biggest companies. This report provides a comprehensive analysis of the market dynamics, technology issues, and competitive landscape for consumer VR HMDs, accessories, and content. The report features global market forecasts for annual unit shipments and associated revenue during the period from 2014 through 2021, segmented by five world regions. HMDs are segmented into four product types: PC-based devices, console-based devices, all-in-one devices, and mobile VR headsets. VR accessories, such as gamepads and other VR-specific controllers, hand tracking devices, and 360° cameras are also quantitatively analyzed. The content market is segmented into gaming and media. - How large is the market opportunity for consumer VR hardware and content? - How will the market be segmented by product type, content type, and world region? - How will this market grow in the coming years and which factors will drive this growth? - Which factors could inhibit growth during the forecast period? - What are the main technology trends and issues in the consumer VR market? - Who are the leading providers of consumer VR technology and how do their go-to-market strategies differ? 2. Market Issues 2.1. Introduction 2.2. Scope of Study 2.2.1. Consumer VR Hardware Scope 2.3. Market Overview 2.4. Market Trends 2.5. Market Drivers 2.5.1. Immersion Experiences 2.5.2. Games Market 2.5.3. Three-Dimensional User Interface 2.5.4. User Interface Shift to Hands/Gesture Control 2.5.5. Smartphone Upgrades 2.5.6. Personal Computer Upgrades 2.5.7. China 2.5.8. VR Video 2.5.9. Mobile Ecosystem/App Stores 2.5.10. Web VR 2.5.11. Cloud Gaming 2.6. Market Barriers 2.6.1. Cost 2.6.2. Complex, Multi-Element Purchase 2.6.3. Quality of Experience 2.6.3.1. Virtual Reality Sickness 2.6.3.2. Restricted Field of View 2.6.3.3. Tethering 2.6.3.4. Lack of Natural User Input 2.6.3.5. Streaming Challenges 2.6.3.6. Corrective Eyewear 2.6.4. Trial and Error for Early Virtual Reality Applications 2.7. Use Cases 2.7.1. Games 2.7.2. Video Media Content 2.7.3. Social VR 2.7.4. Marketing 2.7.4.1. Retail E-Commerce 2.7.4.2. Residential Buying/Renting 2.7.4.3. Travel 2.7.5. Wellness Self Help 2.7.6. Fitness 2.7.7. Spatial Computing 3. Technology Issues 3.1. Introduction 3.2. Tracking 3.2.1. Inside-Out and Outside-In 3.2.1.1. Simultaneous Location and Mapping and Computer Vision 3.2.2. Eye Tracking 3.2.3. Hand Tracking Solutions 3.2.4. Gesture Control 3.3. Field of View 3.4. Latency Technologies and Virtual Reality Sickness Prevention 3.4.1. Galvanic Vestibular Stimulation 3.4.2. Frame Tearing 3.4.2.1. Oculus Asynchronous Timewarp and Spacewarp 3.4.2.2. VIVE Asynchronous Reprojection 3.4.3. Field of View Restrictors 3.5. Display Technology 3.6. Graphics Processing Units 3.7. Cameras 3.8. Three-Dimensional Audio 3.9. Adaptive Streaming 3.9.1. Bitmovin 3.9.2. Pixvana 3.10. Seated versus Moving Experiences 3.10.1. Wireless Connectivity Technologies 3.10.2. Local Rendering 4. Key Industry Players 4.1. Introduction 4.2. Key Head-Mounted Display and Platform Players 4.2.1. HTC 4.2.2. Facebook 4.2.2.1. Content Initiatives 4.2.2.2. Evolving Head-Mounted Displays and Virtual Reality Experience 4.2.2.3. Social Virtual Reality 4.2.3. Sony 4.2.4. Google 4.2.5. Microsoft 4.2.6. Razer 4.2.7. Starbreeze Studios and Acer 4.2.8. NVIDIA 4.2.9. Sulon Technologies 4.3. Key Enabling Technology Players 4.3.1. VisiSonics 4.3.2. Bitmovin 4.3.3. Pixvana 4.3.4. uSens 4.3.5. Leap Motion 4.3.6. vMocion 4.3.7. Binary VR 4.3.8. Improbable 4.3.9. Movidius 4.3.10. VR Lens Lab 4.4. Other Key Players 4.4.1. Vroom 4.4.2. Alibaba 4.4.3. Amazon 4.4.4. NextVR 4.4.5. Wevr 4.4.6. Baobab Studios 4.4.7. Surreal VR 4.4.8. AltspaceVR 4.4.9. nDreams 4.4.10. Unity Technologies 4.4.11. Machina OBE 4.5. Other Selected Industry Participants 5. Market Forecasts 5.1. Introduction 5.2. Data Collection 5.3. Forecast Methodology 5.3.1. Top-Level Head-Mounted Display Shipments 5.3.2. Virtual Reality Accessories 5.3.3. Average Selling Prices and Revenue 5.4. Virtual Reality Mass Market Penetration Estimates 5.5. Top-Level Annual Virtual Reality Revenue 5.6. Annual Virtual Reality Head-Mounted Display Shipments and Revenue 5.7. Annual Virtual Reality Accessories Shipments and Revenue 5.8. Annual Virtual Reality Content Revenue by Content Type 5.9. Consumer Virtual Reality Market by Region 5.10. Conclusions and Recommendations For more information about this report visit http://www.researchandmarkets.com/research/zh6t3m/virtual_reality


Grant
Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2009.3.6 | Award Amount: 3.44M | Year: 2010

The emergence of highly parallel, heterogeneous, often incompatible and highly diverse, many-core processors poses major challenges to the European software-intensive industry. It is imperative that such architectures can be fully exploited without starting from scratch with each new design. In particular, there is an urgent need for techniques for efficient, productive and portable programming of heterogeneous many-cores.\nPEPPHER will provide a unified framework for programming architecturally diverse, heterogeneous many-core processors to ensure performance portability. PEPPHER will advance state-of-the-art in its five technical work areas:\n(1)\tMethods and tools for component based software; (2) Portable compilation techniques; (3) Data structures and adaptive, autotuned algorithms; (4) Efficient, flexible run-time systems; and (5) Hardware support for autotuning, synchronization and scheduling.\nPEPPHER is unique in proposing direct compilation to the target architectures. Portability is supported by powerful composition methods and a toolbox of adaptive algorithms. Heterogeneity is further managed by efficient run-time schedulers. The PEPPHER framework will thus ensure that applications execute with maximum efficiency on each supported platform.\nPEPPHER is driven by challenging benchmarks from the industrial partners. Results will be widely disseminated through high-quality publications, workshops and summer-schools, and an edited volume of major results. Techniques and software prototypes will be exploited by the industrial partners. A project website (www.peppher.eu) gives continuity to the dissemination effort.\nThe PEPPHER consortium unites Europes leading experts and consists of world-class research centres and universities (INRIA, Chalmers, LIU, KIT, TUW, UNIVIE), a major company (Intel) and European multi-core SMEs (Codeplay and Movidius), and has the required expertise to accomplish the ambitious but realistic goals of PEPPHER.


Grant
Agency: European Commission | Branch: H2020 | Program: IA | Phase: ICT-01-2014 | Award Amount: 4.93M | Year: 2015

Vision, our richest sensor, allows inferring big data from reality. Arguably, to be smart everywhere we will need to have eyes everywhere. Coupled with advances in artificial vision, the possibilities are endless in terms of wearable applications, augmented reality, surveillance, ambient-assisted living, etc. Currently, computer vision is rapidly moving beyond academic research and factory automation. On the other hand, mass-market mobile devices owe much of their success to their impressing imaging capabilities, so the question arises if such devices could be used as eyes everywhere. Vision is the most demanding sensor in terms of power consumption and required processing power and, in this respect, existing mass-consumer mobile devices have three problems: 1) power consumption precludes their always-on capability, 2) they would have unused sensors for most vision-based applications and 3) since they have been designed for a definite purpose (i.e. as cell phones, PDAs and readers) people will not consistently use them for other purposes. Our objective in this project is to build an optimized core vision platform that can work independently and also embedded into all types of artefacts. The envisioned open hardware must be combined with carefully designed APIs that maximize inferred information per milliwatt and adapt the quality of inferred results to each particular application. This will not only mean more hours of continuous operation, it will allow to create novel applications and services that go beyond what current vision systems can do, which are either personal/mobile or always-on but not both at the same time. Thus, the Eyes of Things project aims at developing a ground-breaking platform that combines: a) a need for more intelligence in future embedded systems, b) computer vision moving rapidly beyond academic research and factory automation and c) the phenomenal technological advances in mobile processing power.


Grant
Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2013.3.4 | Award Amount: 3.31M | Year: 2013

The EXCESS project aims at providing radically new energy execution models forming foundations for energy-efficient computing paradigms that will enable two orders of magnitude improvements in energy efficiency for computing systems. A holistic approach that involves both hardware and software aspects together has the best chances to successfully address the energy efficiency problem and discover innovative solutions. EXCESS proposed models will try to describe and bridge embedded processors models with general purpose ones. EXCESS will take a holistic approach and will introduce novel programming methodologies to drastically simplify the development of energy-aware applications that will be energy-portable in a wide range of computing systems while preserving relevant aspects of performance.\nThe EXCESS project is going to be driven by the following technical components that are going to be developed during EXCESS:\n Complete software stacks (including programming models, libraries/algorithms and runtimes) for energy- efficient computing.\n Uniform, generic development methodology and prototype software tools that enable leveraging additional optimisation opportunities for energy-efficient computing by coordinating optimisation knobs at the different levels of the system stack, enabled by appropriate modelling abstractions at each level.\n Configurable energy-aware simulation systems for future energy-efficient architectures.\n\nThe EXCES consortium unites Europes leading experts in both high-performance computing and embedded computing. The consortium consists of world-class research centres and universities (Chalmers, LIU, UiT), a high performance computing centre (HLRS at USTUTT), and a European embedded multi-core SME (Movidius), and has the required expertise to accomplish the ambitious but realistic goals of EXCESS.


News Article | December 9, 2016
Site: www.businesswire.com

SAN JOSE, Calif.--(BUSINESS WIRE)--The Global Semiconductor Alliance (GSA) is proud to announce the award recipients honored at the 2016 GSA Awards Dinner Celebration that took place in Santa Clara, California. Over the past 22 years the awards program has recognized the achievements of semiconductor companies in several categories ranging from outstanding leadership to financial accomplishments, as well as overall respect within the industry. The GSA’s most prestigious award, the Dr. Morris Chang Exemplary Leadership Award, was presented to Mr. Lip-Bu Tan, President and CEO of Cadence Design Systems, Inc. and Founder and Chairman of Walden International. GSA members identified the Most Respected Public Semiconductor Company Award winners by casting ballots for the industry’s most respected companies judging by their products, vision and future opportunities. Winners included the “Most Respected Emerging Public Semiconductor Company Achieving $100 Million to $500 Million in Annual Sales Award” presented to Nordic Semiconductor; “Most Respected Public Semiconductor Company Achieving $500 Million to $1 Billion in Annual Sales Award” awarded to Silicon Labs; “Most Respected Public Semiconductor Company Achieving $1 Billion to $5 Billion in Annual Sales Award” awarded to Analog Devices, Inc.; and “Most Respected Public Semiconductor Company Achieving Greater than $5 Billion in Annual Sales Award” received by NVIDIA Corporation. The “Most Respected Private Company Award” was voted on by GSA membership and presented to Quantenna Communications, Inc. Other winners include “Best Financially Managed Company Achieving up to $1 Billion in Annual Sales Award” presented to Silicon Motion Technology Corporation (Silicon Motion, Inc.) and “Best Financially Managed Semiconductor Company Achieving Greater than $1 Billion in Annual Sales Award” earned by NVIDIA Corporation. Both companies were recognized based on their continued demonstration of the best overall financial performance according to specific financial metrics. GSA’s Private Awards Committee, comprised of venture capitalists and select industry entrepreneurs, chose the “Start-Up to Watch Award” winner by identifying a company that has demonstrated the potential to positively change its market or the industry through the innovative use of semiconductor technology or a new application for semiconductor technology. This year’s winner is Innovium, Inc. As a global organization, the GSA recognizes outstanding companies headquartered in the Europe/Middle East/Africa and Asia-Pacific regions. Chosen by the leadership council of each respective region, award winners are semiconductor companies that demonstrate the most strength when measuring products, vision, leadership and success in the marketplace. The recipient of this year’s “Outstanding Asia-Pacific Semiconductor Company Award” is MediaTek Inc. and the recipient of this year’s “Outstanding EMEA Semiconductor Company Award” is Movidius. Semiconductor financial analyst Quinn Bolton from Needham & Company presented this year’s “Favorite Analyst Semiconductor Company Award” to Microsemi Corporation. The criteria used in selecting this year’s winner included historical, as well as projected data, such as stock price, earnings per share, revenue forecasts and product performance. The Global Semiconductor Alliance (GSA) mission is to support the global semiconductor industry and its partners by offering a comprehensive view of the industry. This enables members to better anticipate market opportunities and industry trends, preparing them for technology and business shifts. It addresses the challenges within the supply chain including IP, EDA/design, wafer manufacturing, test and packaging to enable industry-wide solutions. Providing a platform for meaningful global collaboration through efficient power networking for global semiconductor leaders and their partners, the Alliance identifies and articulates market opportunities, encourages and supports entrepreneurship, and provides members with comprehensive and unique market intelligence. Members include companies throughout the supply chain representing 30 countries across the globe. www.gsaglobal.org


News Article | October 28, 2016
Site: www.wired.com

In less than 12 hours, three different people offered to pay me if I’d spend an hour talking to a stranger on the phone. All three said they’d enjoyed reading an article I’d written about Google building a new computer chip for artificial intelligence, and all three urged me to discuss the story with one of their clients. Each described this client as the manager of a major hedge fund, but wouldn’t say who it was. The requests came from what are called expert networks—research firms that connect investors with people who can help them understand particular markets and provide a competitive edge (sometimes, it seems, through insider information). These expert networks wanted me to explain how Google’s AI processor would affect the chip market. But first, they wanted me to sign a non-disclosure agreement. I declined. These unsolicited, extremely specific, high-pressure requests—which arrived about three week ago—underscore the radical changes underway in the enormously lucrative computer chip market, changes driven by the rise of artificial intelligence. Those hedge fund managers see these changes coming, but aren’t quite sure how they’ll play out. Of course, no one is quite sure how they’ll play out. Today, Internet giants like Google, Facebook, Microsoft, Amazon, and China’s Baidu are exploring a wide range of chip technologies that can drive AI forward, and the choices they make will shift the fortunes of chipmakers like Intel and nVidia. But at this point, even the computer scientists within those online giants don’t know what the future holds. These companies run their online services from data centers packed with thousands of servers, each driven by a chip called a central processing unit, or CPU. But as they embrace a form of AI called deep neural networks, these companies are supplementing CPUs with other processors. Neural networks can learn tasks by analyzing vast amounts of data, including everything from identifing faces and objects in photos to translating between languages, and they require more than just CPU power. And so Google built the Tensor Processing Unit, or TPU. Microsoft is using a processor called a field programmable gate array, or FPGA. Myriad companies employ machines equipped with vast numbers of graphics processing units, or GPUs. And they’re all looking at a new breed of chip that could accelerate AI from inside smartphones and other devices. Any choice these companies make matters, because their online operations are so vast. They buy and operate far more computer hardware than anyone else on Earth, a gap that will only widen with the continued importance of cloud computing.  If Google chooses one processor over another, it can fundamentally shift the chip industry. The TPU poses a threat to companies like Intel and nVidia because Google makes this chip itself. But GPUs also play an enormous role within Google and its ilk, and nVidia is the primary manufacturer of these specialized chips. Meanwhile, Intel has inserted itself into the mix by acquiring Altera, the company that sells all those FPGAs to Microsoft. At $16.7 billion, it was Intel’s largest acquisition ever, which underscores just how much the chip market is changing. But sorting all this out is difficult—in part because neutral networks operate in two stages. The first is the training stage, where a company like Google trains the neural network to perform a given task, like recognizing faces in photos or translating from one language to another. The second is the execution stage, where people like you and me actually use the neural net—where we, say, post a photo of our high school reunion to Facebook and it automatically tags everyone in it. These two stages are quite different, and each requires a different style of processing. Today, GPUs are the best option for training. Chipmakers designed GPUs to render images for games and other highly graphical applications, but in recent years, companies like Google discovered these chips can also provide an energy-efficient means of juggling the mind-boggling array of calculations required to train a neural network. This means they can train more neural nets with less hardware. Microsoft AI researcher XD Huang calls GPUs “the real weapon.” Recently, his team completed a system that can recognize certain conversational speech as well as humans, and it took them about a year. Without GPUs, he says, it would have taken five. After Microsoft published a research paper on this system, he opened a bottle of champagne at the home of Jen-Hsun Huang, the CEO of nVidia. But companies also need chips that can rapidly execute neural networks, a process called inference. Google built the TPU specifically for this. Microsoft uses FPGAs. And Baidu is using GPUs, which aren’t as well suited to inference as they are to training, but can do the job with the right software in place. At the same time, others are building chips to help execute neural networks on smartphones and other devices. IBM is building such a chip, though some wonder how effective it might be. And Intel has agreed to acquire Movidius, a company that is already pushing chips into devices. Intel understands that the market is changing. Four years ago, the chip maker told us it sells more server processors to Google than it sells to all but four other companies—so it sees firsthand how Google and its ilk can shift the chip market. As a result, it’s now placing bets everywhere. Beyond snapping up Altera and Movidius, it has agreed to buy a third AI chip company called Nervana. That makes sense, because the market is only starting to develop. “We’re now at the precipice of the next big wave of growth,” Intel vice president Jason Waxman recently told me, “and that’s going to be driven by artificial intelligence.” The question is where the wave will take us.


News Article | August 26, 2016
Site: phys.org

Energy consumption is one of the key challenges of modern computing, whether for wireless embedded client devices or high performance computing centers. The ability to develop energy efficient software is crucial, as the use of data and data processing keeps increasing in all areas of society. The need for power efficient computing is not only due to the environmental impact. Rather, we need energy efficient computing in order to even deliver on the trends predicted. The EU funded Excess project, which finishes August 31, set out three years ago to take on what the researchers perceived as a lack of holistic, integrated approaches to cover all system layers from hardware to user level software, and the limitations this caused to the exploitation of the existing solutions and their energy efficiency. They initially analyzed where energy-performance is wasted, and based on that knowledge they have developed a framework that should allow for rapid development of energy efficient software production. "When we started this research program there was a clear lack of tools and mathematical models to help the software engineers to program in an energy efficient way, and also to reason abstractly about the power and energy behavior of her software" says Philippas Tsigas, professor in Computer Engineering at Chalmers University of Technology, and project leader of Excess. "The holistic approach of the project involves both hardware and software components together, enabling the programmer to make power-aware architectural decisions early. This allows for larger energy savings than previous approaches, where software power optimization was often applied as a secondary step, after the initial application was written." The Excess project has taken major steps towards providing a set of tools and models to software developers and system designers to allow them to program in an energy efficient way. The tool box spans from fundamentally new energy-saving hardware components, such as the Movidius Myriad platform, to sophisticated efficient libraries and algorithms. Tests run on large data streaming aggregations, a common operation used in real-time data analytics, has shown impressive results. When using the Excess framework, the programmer can provide a 54 times more energy efficient solution compared to a standard implementation on a high-end PC processor. The holistic Excess approach first presents the hardware benefits, using an embedded processor, and then continues to show the best way to split the computations inside the processor, to even further enhance the performance. Movidius, a partner in the Excess project and developers of the Myriad platform of vision processors, has integrated both technology and methodology developed in the project into their standard development kit hardware and software offering. In the embedded processor business, there has been a gradual migration of HPC class features getting deployed on embedded platforms. The rapid development in autonomous vehicles such as cars and drones, driving assist systems, and also the general development of home assist robotics (e.g. vacuum cleaners and lawnmowers) has led to the porting of various computer vision algorithms to embedded platforms. Traditionally these algorithms were developed on high performance desktop computers and HPC systems, making them difficult to re-deploy to embedded systems. Another problem was that the algorithms were not developed with energy efficiency in mind. But the Excess project has enabled and directed the development of tools and software development methods to aid the porting of HPC applications to the embedded environment in an energy efficient way. Explore further: Better software cuts computer energy use

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