#FPGA Companies

[chandupalle]

[364 Pages Report] The FPGA market was valued at USD 12.1 billion in 2024 and is estimated to reach USD 25.8 billion by 2029, registering a CAGR of 16.4% during the forecast period.

[volersystems]

Apart from the FPGA design, Voler Systems formulated the necessary firmware for board functionality testing that enabled to customer to finalize their firmware development. Voler Systems worked closely with their mechanical design team to match the device’s electrical, mechanical, and environmental requirements. Their engineers made sure that the device was functional, durable, and reliable under the extreme conditions, often common during military operations.

[andmaybegayer]

What are some of the coolest computer chips ever, in your opinion?

Hmm. There are a lot of chips, and a lot of different things you could call a Computer Chip. Here's a few that come to mind as "interesting" or "important", or, if I can figure out what that means, "cool".

If your favourite chip is not on here honestly it probably deserves to be and I either forgot or I classified it more under "general IC's" instead of "computer chips" (e.g. 555, LM, 4000, 7000 series chips, those last three each capable of filling a book on their own). The 6502 is not here because I do not know much about the 6502, I was neither an Apple nor a BBC Micro type of kid. I am also not 70 years old so as much as I love the DEC Alphas, I have never so much as breathed on one.

Disclaimer for writing this mostly out of my head and/or ass at one in the morning, do not use any of this as a source in an argument without checking.

Intel 3101

So I mean, obvious shout, the Intel 3101, a 64-bit chip from 1969, and Intel's first ever product. You may look at that, and go, "wow, 64-bit computing in 1969? That's really early" and I will laugh heartily and say no, that's not 64-bit computing, that is 64 bits of SRAM memory.

This one is cool because it's cute. Look at that. This thing was completely hand-designed by engineers drawing the shapes of transistor gates on sheets of overhead transparency and exposing pieces of crudely spun silicon to light in a """"cleanroom"""" that would cause most modern fab equipment to swoon like a delicate Victorian lady. Semiconductor manufacturing was maturing at this point but a fab still had more in common with a darkroom for film development than with the mega expensive building sized machines we use today.

As that link above notes, these things were really rough and tumble, and designs were being updated on the scale of weeks as Intel learned, well, how to make chips at an industrial scale. They weren't the first company to do this, in the 60's you could run a chip fab out of a sufficiently well sealed garage, but they were busy building the background that would lead to the next sixty years.

Lisp Chips

This is a family of utterly bullshit prototype processors that failed to be born in the whirlwind days of AI research in the 70's and 80's.

Lisps, a very old but exceedingly clever family of functional programming languages, were the language of choice for AI research at the time. Lisp compilers and interpreters had all sorts of tricks for compiling Lisp down to instructions, and also the hardware was frequently being built by the AI researchers themselves with explicit aims to run Lisp better.

The illogical conclusion of this was attempts to implement Lisp right in silicon, no translation layer.

Yeah, that is Sussman himself on this paper.

These never left labs, there have since been dozens of abortive attempts to make Lisp Chips happen because the idea is so extremely attractive to a certain kind of programmer, the most recent big one being a pile of weird designd aimed to run OpenGenera. I bet you there are no less than four members of r/lisp who have bought an Icestick FPGA in the past year with the explicit goal of writing their own Lisp Chip. It will fail, because this is a terrible idea, but damn if it isn't cool.

There were many more chips that bridged this gap, stuff designed by or for Symbolics (like the Ivory series of chips or the 3600) to go into their Lisp machines that exploited the up and coming fields of microcode optimization to improve Lisp performance, but sadly there are no known working true Lisp Chips in the wild.

Zilog Z80

Perhaps the most important chip that ever just kinda hung out. The Z80 was almost, almost the basis of The Future. The Z80 is bizzare. It is a software compatible clone of the Intel 8080, which is to say that it has the same instructions implemented in a completely different way.

This is, a strange choice, but it was the right one somehow because through the 80's and 90's practically every single piece of technology made in Japan contained at least one, maybe two Z80's even if there was no readily apparent reason why it should have one (or two). I will defer to Cathode Ray Dude here: What follows is a joke, but only barely

The Z80 is the basis of the MSX, the IBM PC of Japan, which was produced through a system of hardware and software licensing to third party manufacturers by Microsoft of Japan which was exactly as confusing as it sounds. The result is that the Z80, originally intended for embedded applications, ended up forming the basis of an entire alternate branch of the PC family tree.

It is important to note that the Z80 is boring. It is a normal-ass chip but it just so happens that it ended up being the focal point of like a dozen different industries all looking for a cheap, easy to program chip they could shove into Appliances.

Effectively everything that happened to the Intel 8080 happened to the Z80 and then some. Black market clones, reverse engineered Soviet compatibles, licensed second party manufacturers, hundreds of semi-compatible bastard half-sisters made by anyone with a fab, used in everything from toys to industrial machinery, still persisting to this day as an embedded processor that is probably powering something near you quietly and without much fuss. If you have one of those old TI-86 calculators, that's a Z80. Oh also a horrible hybrid Z80/8080 from Sharp powered the original Game Boy.

I was going to try and find a picture of a Z80 by just searching for it and look at this mess! There's so many of these things.

I mean the C/PM computers. The ZX Spectrum, I almost forgot that one! I can keep making this list go! So many bits of the Tech Explosion of the 80's and 90's are powered by the Z80. I was not joking when I said that you sometimes found more than one Z80 in a single computer because you might use one Z80 to run the computer and another Z80 to run a specialty peripheral like a video toaster or music synthesizer. Everyone imaginable has had their hand on the Z80 ball at some point in time or another. Z80 based devices probably launched several dozen hardware companies that persist to this day and I have no idea which ones because there were so goddamn many.

The Z80 eventually got super efficient due to process shrinks so it turns up in weird laptops and handhelds! Zilog and the Z80 persist to this day like some kind of crocodile beast, you can go to RS components and buy a brand new piece of Z80 silicon clocked at 20MHz. There's probably a couple in a car somewhere near you.

Pentium (P6 microarchitecture)

Yeah I am going to bring up the Hackers chip. The Pentium P6 series is currently remembered for being the chip that Acidburn geeks out over in Hackers (1995) instead of making out with her boyfriend, but it is actually noteworthy IMO for being one of the first mainstream chips to start pulling serious tricks on the system running it.

The P6 microarchitecture comes out swinging with like four or five tricks to get around the numerous problems with x86 and deploys them all at once. It has superscalar pipelining, it has a RISC microcode, it has branch prediction, it has a bunch of zany mathematical optimizations, none of these are new per se but this is the first time you're really seeing them all at once on a chip that was going into PC's.

Without these improvements it's possible Intel would have been beaten out by one of its competitors, maybe Power or SPARC or whatever you call the thing that runs on the Motorola 68k. Hell even MIPS could have beaten the ageing cancerous mistake that was x86. But by discovering the power of lying to the computer, Intel managed to speed up x86 by implementing it in a sensible instruction set in the background, allowing them to do all the same clever pipelining and optimization that was happening with RISC without having to give up their stranglehold on the desktop market. Without the P5 we live in a very, very different world from a computer hardware perspective.

From this falls many of the bizzare microcode execution bugs that plague modern computers, because when you're doing your optimization on the fly in chip with a second, smaller unix hidden inside your processor eventually you're not going to be cryptographically secure.

RISC is very clearly better for, most things. You can find papers stating this as far back as the 70's, when they start doing pipelining for the first time and are like "you know pipelining is a lot easier if you have a few small instructions instead of ten thousand massive ones.

x86 only persists to this day because Intel cemented their lead and they happened to use x86. True RISC cuts out the middleman of hyperoptimizing microcode on the chip, but if you can't do that because you've girlbossed too close to the sun as Intel had in the late 80's you have to do something.

The Future

This gets us to like the year 2000. I have more chips I find interesting or cool, although from here it's mostly microcontrollers in part because from here it gets pretty monotonous because Intel basically wins for a while. I might pick that up later. Also if this post gets any longer it'll be annoying to scroll past. Here is a sample from a post I have in my drafts since May:

I have some notes on the weirdo PowerPC stuff that shows up here it's mostly interesting because of where it goes, not what it is. A lot of it ends up in games consoles. Some of it goes into mainframes. There is some of it in space. Really got around, PowerPC did.

[govindhtech]

Agilex 3 FPGAs: Next-Gen Edge-To-Cloud Technology At Altera

Agilex 3 FPGA

Today, Altera, an Intel company, launched a line of FPGA hardware, software, and development tools to expand the market and use cases for its programmable solutions. Altera unveiled new development kits and software support for its Agilex 5 FPGAs at its annual developer’s conference, along with fresh information on its next-generation, cost-and power-optimized Agilex 3 FPGA.

Altera

Why It Matters

Altera is the sole independent provider of FPGAs, offering complete stack solutions designed for next-generation communications infrastructure, intelligent edge applications, and high-performance accelerated computing systems. Customers can get adaptable hardware from the company that quickly adjusts to shifting market demands brought about by the era of intelligent computing thanks to its extensive FPGA range. With Agilex FPGAs loaded with AI Tensor Blocks and the Altera FPGA AI Suite, which speeds up FPGA development for AI inference using well-liked frameworks like TensorFlow, PyTorch, and OpenVINO toolkit and tested FPGA development flows, Altera is leading the industry in the use of FPGAs in AI inference workload

Intel Agilex 3

What Agilex 3 FPGAs Offer

Designed to satisfy the power, performance, and size needs of embedded and intelligent edge applications, Altera today revealed additional product details for its Agilex 3 FPGA. Agilex 3 FPGAs, with densities ranging from 25K-135K logic elements, offer faster performance, improved security, and higher degrees of integration in a smaller box than its predecessors.

An on-chip twin Cortex A55 ARM hard processor subsystem with a programmable fabric enhanced with artificial intelligence capabilities is a feature of the FPGA family. Real-time computation for time-sensitive applications such as industrial Internet of Things (IoT) and driverless cars is made possible by the FPGA for intelligent edge applications. Agilex 3 FPGAs give sensors, drivers, actuators, and machine learning algorithms a smooth integration for smart factory automation technologies including robotics and machine vision.

Agilex 3 FPGAs provide numerous major security advancements over the previous generation, such as bitstream encryption, authentication, and physical anti-tamper detection, to fulfill the needs of both defense and commercial projects. Critical applications in industrial automation and other fields benefit from these capabilities, which guarantee dependable and secure performance.

Agilex 3 FPGAs offer a 1.9×1 boost in performance over the previous generation by utilizing Altera’s HyperFlex architecture. By extending the HyperFlex design to Agilex 3 FPGAs, high clock frequencies can be achieved in an FPGA that is optimized for both cost and power. Added support for LPDDR4X Memory and integrated high-speed transceivers capable of up to 12.5 Gbps allow for increased system performance.

Agilex 3 FPGA software support is scheduled to begin in Q1 2025, with development kits and production shipments following in the middle of the year.

How FPGA Software Tools Speed Market Entry

Quartus Prime Pro

The Latest Features of Altera’s Quartus Prime Pro software, which gives developers industry-leading compilation times, enhanced designer productivity, and expedited time-to-market, are another way that FPGA software tools accelerate time-to-market. With the impending Quartus Prime Pro 24.3 release, enhanced support for embedded applications and access to additional Agilex devices are made possible.

Agilex 5 FPGA D-series, which targets an even wider range of use cases than Agilex 5 FPGA E-series, which are optimized to enable efficient computing in edge applications, can be designed by customers using this forthcoming release. In order to help lower entry barriers for its mid-range FPGA family, Altera provides software support for its Agilex 5 FPGA E-series through a free license in the Quartus Prime Software.

Support for embedded applications that use Altera’s RISC-V solution, the Nios V soft-core processor that may be instantiated in the FPGA fabric, or an integrated hard-processor subsystem is also included in this software release. Agilex 5 FPGA design examples that highlight Nios V features like lockstep, complete ECC, and branch prediction are now available to customers. The most recent versions of Linux, VxWorks, and Zephyr provide new OS and RTOS support for the Agilex 5 SoC FPGA-based hard processor subsystem.

How to Begin for Developers

In addition to the extensive range of Agilex 5 and Agilex 7 FPGAs-based solutions available to assist developers in getting started, Altera and its ecosystem partners announced the release of 11 additional Agilex 5 FPGA-based development kits and system-on-modules (SoMs).

Developers may quickly transition to full-volume production, gain firsthand knowledge of the features and advantages Agilex FPGAs can offer, and easily and affordably access Altera hardware with FPGA development kits.

Kits are available for a wide range of application cases and all geographical locations. To find out how to buy, go to Altera’s Partner Showcase website.

Read more on govindhtech.com

[moremarketresearch]

Global AI Accelerator Chip Market Expected to Grow Substantially Owing to Healthcare Industry

Global AI Accelerator Chip Market Expected to Grow Substantially Owing to Increased Use of AI Accelerator Chips in Healthcare Industry. The global AI accelerator chip market is expected to grow primarily due to its growing use in the healthcare industry. The cloud sub-segment is expected to flourish immensely. The market in the North American region is predicted to grow with a high CAGR by 2031. NEW YORK, March 17, 2023 - As per the report published by Research Dive, the global AI accelerator chip market is expected to register a revenue of $332,142.7 million by 2031 with a CAGR of 39.3% during the 2022-2031 period.

Dynamics of the Global AI Accelerator Chip Market

Growing use of AI accelerator chips across the global healthcare industry is expected to become the primary growth driver of the AI accelerator chip market in the forecast period. Additionally, the rise of the cyber safety business is predicted to propel the market forward. However, according to market analysts, lack of skilled AI accelerator chip workforce might become a restraint in the growth of the market. The growing use of AI accelerator chip semiconductors is predicted to offer numerous growth opportunities to the market in the forecast period. Moreover, the increased use of AI accelerator chips to execute AI workloads such as neural networks is expected to propel the AI accelerator chip market forward in the coming period.

COVID-19 Impact on the Global AI Accelerator Chip Market

The Covid-19 pandemic disrupted the routine lifestyle of people across the globe and the subsequent lockdowns adversely impacted the industrial processes across all sectors. The AI accelerator chip market, too, was negatively impacted due to the pandemic. The disruptions in global supply chains due to the pandemic resulted in a decline in the semiconductor manufacturing industry. Also, the travel restrictions put in place by various governments reduced the availability of skilled workforce. These factors brought down the growth rate of the market.

Key Players of the Global AI Accelerator Chip Market

The major players in the market include: - NVIDIA Corporation - Micron Technology Inc. - NXP Semiconductors N.V. - Intel Corporation - Microsoft Corporation - Advanced Micro Devices Inc. (AMD) - Qualcomm Technologies Inc. - Alphabet Inc. (Google Inc.) - Graphcore Limited. - International Business Machines Corporation These players are working on developing strategies such as product development, merger and acquisition, partnerships, and collaborations to sustain market growth. For instance, in May 2022, Intel Habana, a subsidiary of Intel, announced the launch of 2nd generation AI chips which according to the company, will provide a 2X performance advantage over the previous generation NVIDIA A100. This product launch will help Intel Habana to capitalize on this rather nascent market and will consolidate its lead over the competitors further.

What the Report Covers:

Apart from the information summarized in this press release, the final report covers crucial aspects of the market including SWOT analysis, market overview, Porter's five forces analysis, market dynamics, segmentation (key market trends, forecast analysis, and regional analysis), and company profiles (company overview, operating business segments, product portfolio, financial performance, and latest strategic moves and developments.)

Segments of the AI Accelerator Chip Market

The report has divided the AI accelerator chip market into the following segments: Chip Type: Graphics Processing Unit (GPU), Application-Specific Integrated Circuit (ASIC), Field Programmable Gate Arrays (FPGA), Central Processing Unit (CPU), and others Processing Type: edge and cloud Application: Natural Language Processing (NLP), computer vision, robotics, and network security Industry Vertical: financial services, automotive and transportation, healthcare, retail, telecom, and others Region: North America, Europe, Asia-Pacific, and LAMEA SegmentSub-SegmentChip TypeCentral Processing Unit (CPU) – Most dominant market share in 2021 - The use of CPU for improving the performance of a computer while running graphics and video editors are expected to push the growth of this sub-segment further.Processing TypeCloud – Significant revenue growth in 2021 Cloud acceleration chip helps content creators, publishers, and other entities to offer material to end users promptly which is predicted to propel the growth rate of the market higher.ApplicationNatural Language Processing (NLP) – Highest market share in 2021 Increased use of Natural Language Processing (NLP) due to its ability to make computer-human interactions more natural is expected to propel the sub-segment forward.Industry VerticalHealthcare– Huge market revenue in 2021 The growing use of AI by major healthcare companies to complement medical imaging is anticipated to offer numerous growth opportunities to the sub-segment in the forecast period.RegionNorth America – Most profitable by 2031 The development of new technologies in artificial intelligence (AI) accelerators in this region is predicted to propel the market in the forecast period. Read the full article

[anidealvenue]

A list of Automotive Engineering Service Companies in Germany

Bertrandt AG, https://www.bertrandt.com/. Bertrandt operates in digital engineering, physical engineering, and electrical systems/electronics segments. Its Designing function includes designing of all the elements of the automotive.

Alten Group, https://www.alten.com/. ALTEN Group supports the development strategy of its customers in the fields of innovation, R&D and technological information systems. Created 30 years ago, the Group has become a world leader in Engineering and Technology consulting. 24 700 highly qualified engineers carry out studies and conception projects for the Technical and Information Systems Divisions of major customers in the industrial, telecommunications and Service sectors.

L&T Technology Services Limited, https://www.ltts.com/. LTTS’ expertise in engineering design, product development, smart manufacturing, and digitalization touches every area of our lives — from the moment we wake up to when we go to bed. With 90 Innovation and R&D design centers globally, we specialize in disruptive technology spaces such as EACV, Med Tech, 5G, AI and Digital Products, Digital Manufacturing, and Sustainability.

FEV Group GmbH, https://www.fev.com/. FEV is into the design and development of internal combustion engines, conventional, electric, and alternative vehicle drive systems, energy technology, and a major supplier of advanced testing and instrumentation products and services to some of the world’s largest powertrain OEMs. Founded in 1978 by Prof. Franz Pischinger, today the company employs worldwide highly skilled research and development specialists on several continents.

Harman International, https://www.harman.com/. HARMAN designs and engineers connected products and solutions for automakers, consumers, and enterprises worldwide, including connected car systems, audio and visual products, enterprise automation solutions; and services supporting the Internet of Things.

EDAG Engineering GmbH, https://www.edag.com/de/. EDAG is into vehicle development, plant planning and construction, and process optimization.

HCL Technologies Limited, http://www.hcltech.com/. HCL Technologies Limited is an Indian multinational information technology services and consulting company headquartered in Noida. It emerged as an independent company in 1991 when HCL entered into the software services business. The company has offices in 52 countries and over 210,966 employees.

Cientra GmbH, https://www.cientra.com/. Cientra expertise across VLSI, ASIC, FPGA, SoC engineering, and IoT accelerate our delivery of customized solutions to the Consumer, Aviation, Semiconductors, Telecom, Wireless, and Automotive industries across their product lifecycle.

Akka Technologies, https://www.akka-technologies.com/. AKKA supports the world’s leading industry players in their digital transformation and throughout their entire product life cycle.

IAV GmbHb, https://www.iav.com/en/. IAV develops the mobility of the future. Regardless of the specific manufacturer, our engineering proves itself in vehicles and technologies all over the world.

Altran Technologies, https://www.altran.com/in/en/. Altran expertise from strategy and design to managing operations in the fields of cloud, data artificial intelligence, connectivity, software, digital engineering, and platforms.

Capgemini Engineering, https://capgemini-engineering.com/de/de/. Capgemini Engineering is a technology and innovation consultancy across sectors including Aeronautics, Space, Defense, Naval, Automotive, Rail, Infrastructure & Transportation, Energy, Utilities & Chemicals, Life Sciences, Communications, Semiconductor & Electronics, Industrial & Consumer, Software & Internet.

[jcmarchi]

Ubitium Secures $3.7M to Revolutionize Computing with Universal RISC-V Processor

New Post has been published on https://thedigitalinsider.com/ubitium-secures-3-7m-to-revolutionize-computing-with-universal-risc-v-processor/

Ubitium Secures $3.7M to Revolutionize Computing with Universal RISC-V Processor

Ubitium, a semiconductor startup, has unveiled a groundbreaking universal processor that promises to redefine how computing workloads are managed. This innovative chip consolidates processing capabilities into a single, efficient unit, eliminating the need for specialized processors such as CPUs, GPUs, DSPs, and FPGAs. By breaking away from traditional processing architectures, Ubitium is set to simplify computing, slash costs, and enable advanced AI at no additional expense.

The company has secured $3.7 million in seed funding to accelerate the development of this revolutionary technology. Investors Runa Capital, Inflection, and KBC Focus Fund are backing Ubitium’s vision to disrupt the $500 billion processor market and introduce a truly universal processor that makes computing accessible and efficient across industries.

Revolutionizing a $700 Billion Industry

The global semiconductor market, already valued at $574 billion in 2022, is projected to exceed $700 billion by 2025, fueled by increasing demand for AI, IoT, and edge computing solutions. However, traditional processing architectures have struggled to keep up with evolving demands, often relying on specialized chips that inflate costs and complicate system integration.

Ubitium addresses these challenges with its workload-agnostic universal processor, which uses the same transistors for multiple tasks, maximizing efficiency and minimizing waste. This approach not only reduces the size and cost of processors but also simplifies system architecture, making advanced AI capabilities viable even in cost-sensitive industries like consumer electronics and smart farming.

A RISC-V Revolution

The foundation of Ubitium’s processor is the open RISC-V instruction set architecture (ISA). Unlike proprietary ISAs, RISC-V fosters innovation by allowing companies to build on an open standard. Ubitium leverages this flexibility to ensure its processors are compatible with existing software ecosystems, removing one of the biggest barriers to adoption for new computing platforms.

Ubitium’s processors require no proprietary toolchains or specialized software, making them accessible to a wide range of developers. This not only accelerates development cycles but also reduces costs for businesses deploying AI and advanced computing solutions.

An Experienced Team Driving Change

Ubitium’s leadership team brings together decades of experience in semiconductor innovation and business strategy. CTO Martin Vorbach, who holds over 200 semiconductor patents, spent 15 years developing the technology behind Ubitium’s universal processor. His expertise in reconfigurable computing and workload-agnostic architectures has been instrumental in creating a processor that can adapt to any task without the need for multiple specialized cores.

CEO Hyun Shin Cho, an alumnus of the Karlsruhe Institute of Technology, has over 20 years of experience across industrial sectors. His strategic leadership has been key in assembling a world-class team and securing the necessary funding to bring this transformative technology to market.

Chairman Peter Weber, with a career spanning Intel, Texas Instruments, and Dialog Semiconductor, brings extensive industry expertise to guide Ubitium’s mission of democratizing high-performance computing.

Investor Confidence in Ubitium

The $3.7 million seed funding round reflects strong investor confidence in Ubitium’s disruptive potential. Dmitry Galperin, General Partner at Runa Capital, emphasized the adaptability of Ubitium’s processor, which can handle workloads ranging from simple control tasks to massive parallel data flow processing.

Rudi Severijns of KBC Focus Fund highlighted the reduced complexity and faster time-to-market enabled by Ubitium’s architecture, describing it as a game-changer for hardware and software integration. Jonatan Luther-Bergquist of Inflection called Ubitium’s approach a “contrarian bet” on generalized compute capacity in a landscape dominated by chip specialization.

Addressing Key Market Challenges

One of the major barriers to deploying advanced computing solutions is the high cost and complexity of specialized hardware. Ubitium’s universal processor removes this hurdle by offering a single-chip solution that is adaptable to any computing task. This is especially critical for industries where cost sensitivity and rapid deployment are paramount.

For example, in the automotive sector, where AI-powered systems like autonomous driving and advanced driver-assistance systems (ADAS) are becoming standard, Ubitium’s processors can streamline development and reduce costs. Similarly, in industrial automation and robotics, the universal processor simplifies system architectures, enabling faster deployment of intelligent machines.

Applications Across Industries

Ubitium’s universal processor is designed for scalability, making it suitable for a wide range of applications:

Consumer Electronics: Enables smarter, more cost-effective devices with enhanced AI capabilities.

IoT and Smart Farming: Provides real-time intelligence for connected devices, optimizing resource use and increasing efficiency.

Robotics and Industrial Automation: Simplifies the deployment of intelligent machines, reducing time-to-market for robotics solutions.

Space and Defense: Delivers high-performance computing in challenging environments where reliability and adaptability are critical.

Future Roadmap

Ubitium is not stopping with a single chip. The company plans to develop a portfolio of processors that vary in size and performance while sharing the same architecture and software stack. This approach allows customers to scale their applications without changing development processes, ensuring seamless integration across devices of all sizes.

The ultimate goal is to establish Ubitium’s universal processor as the standard platform for computing, breaking down the barriers of cost and complexity that have historically limited the adoption of AI and advanced computing technologies.

Transforming Human-Machine Interaction

Ubitium envisions a future where machines interact naturally with humans and each other, making intelligent decisions in real time. The flexibility of its processors enables the deployment of advanced AI algorithms, such as object detection, natural language processing, and generative AI, across industries.

This shift not only transforms the way we interact with technology but also democratizes access to high-performance computing, enabling innovation at all levels.

[rohanisblog]

Edge AI Processor Market Value to Hit $9.89 Billion by 2032 | Industry Forecast

Astute Analytica has released a comprehensive report titled Global Edge AI Processor Market – Global Industry Trends, Share, Size, Growth, Opportunity and Forecast 2024-2032. This report provides an in-depth examination of the industry, including valuable insights into market analysis, competition, and geographical research. It also highlights recent developments in the global industry. 

Market Overview and Forecast 

The Global edge AI processor market was valued at US$ 2,163.2 million in 2023 and is projected to hit the market valuation of US$ 9,891.5 million by 2032 at a CAGR of 18.4% during the forecast period 2024–2032.

In addition to market positioning, the report offers a thorough analysis of relevant data, key developments, and revenue streams. It outlines the strategies employed by key market players to expand their market presence and strengthen their positions. The report includes detailed information that illustrates the overall market condition.

A Request of this Sample PDF File@- https://www.astuteanalytica.com/request-sample/edge-ai-processor-market

Key Insights 

The report emphasizes future trends, market dynamics, market shares, threats, opportunities, and entry barriers. Important analytical data is presented through pie charts, graphs, and tables, providing readers with a clear understanding of the market landscape. 

Marketing Channels and Supply Chain 

Special attention is given to marketing channels, downstream client surveys, upstream raw materials analysis, and market development trends. The report also includes expert recommendations and crucial information about major chemical suppliers, manufacturers, key consumers, distributors, and dealers, along with their contact details. This information is essential for conducting a detailed market chain analysis. 

Geographical Analysis 

The report features detailed investigations into the global market across various regions, analyzing over 20 countries that significantly contribute to market development. Key regional markets studied include North America, Europe, Asia Pacific, South America, Africa, the Middle East, and Latin America. This thorough examination aids in identifying regional market opportunities and challenges. 

Competitive Analysis 

To illustrate the competitive landscape, the report differentiates business attributes and identifies leading market players. It includes the latest trends, company profiles, financial standings, and SWOT analyses of major Edge AI Processor market players, providing a comprehensive view of the competitive environment. 

Key Players 

Advanced Micro Devices, Inc.

Huawei Technologies

IBM

Intel Corporation

Hailo

NVIDIA Corporation

Mythic

MediaTek Inc.

Graphcore

STMicroelectronics

Other Prominent Companies

For Purchase Enquiry: https://www.astuteanalytica.com/industry-report/edge-ai-processor-market

Methodology 

The global Edge AI Processor analysis is based on primary and secondary data sources. Primary sources include expert interviews with industry analysts, distributors, and suppliers, while secondary sources encompass statistical data reviews from government websites, press releases, and annual reports. Both data types validate the findings from global market leaders. The report utilizes top-down and bottom-up approaches to analyze estimates for each segment. 

Market Segmentation  

By Processor Type

Central Processing Unit (CPU)

Graphics Processing Unit (GPU)

Field Programmable Gate Arrays (FPGA)

Application Specific Integrated Circuits (ASIC)

By Device Type

Consumer Devices

Enterprise Devices

By Application

Robotics

Smartphones and Mobile Devices

Internet of Things (IoT) Devices

Smart Cameras and Surveillance Systems

Autonomous Vehicles

Industrial Automation

Others

By End User

Consumer Electronics

Healthcare

Automotive

Retail

Security and Surveillance

Government

Agriculture

Others (Manufacturing, Construction, etc.)

By Region

North America

The U.S.

Canada

Mexico

Europe

Western Europe

The UK

Germany

France

Italy

Spain

Rest of Western Europe

Eastern Europe

Poland

Russia

Rest of Eastern Europe

Asia Pacific

China

India

Japan

Australia & New Zealand

South Korea

ASEAN

Rest of Asia Pacific

Middle East & Africa (MEA)

Saudi Arabia

South Africa

UAE

Rest of MEA

South America

Argentina

Brazil

Rest of South America

Download Sample PDF Report@- https://www.astuteanalytica.com/request-sample/edge-ai-processor-market

About Astute Analytica:

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[chandupalle]

FPGA Companies - Advanced Micro Devices (Xilinx, Inc.) (US) and Intel Corporation (US) are the Key Players

 The FPGA market is projected to grow from USD 12.1 billion in 2024 and is projected to reach USD 25.8 billion by 2029; it is expected to grow at a CAGR of 16.4% from 2024 to 2029.

The growth of the FPGA market is driven by the rising trend towards Artificial Intelligence (AI) and Internet of Things (IoT) technologies in various applications and the integration of FPGAs into advanced driver assistance systems (ADAS). 

Major FPGA companies include:

·         Advanced Micro Devices (Xilinx, Inc.) (US),

·         Intel Corporation (US),

·         Microchip Technology Inc. (US),

·         Lattice Semiconductor Corporation (US), and

·         Achronix Semiconductor Corporation (US).

Major strategies adopted by the players in the FPGA market ecosystem to boost their product portfolios, accelerate their market share, and increase their presence in the market include acquisitions, collaborations, partnerships, and new product launches.

For instance, in October 2023, Achronix Semiconductor Corporation announced a partnership with Myrtle.ai, introducing an accelerated automatic speech recognition (ASR) solution powered by the Speedster7t FPGA. This innovation enables the conversion of spoken language into text in over 1,000 real-time streams, delivering exceptional accuracy and response times, all while outperforming competitors by up to 20 times.

In May 2023, Intel Corporation introduced the Agilex 7 featuring the R-Tile chiplet. Compared to rival FPGA solutions, Agilex 7 FPGAs equipped with the R-Tile chiplet showcase cutting-edge technical capabilities, providing twice the speed in PCIe 5.0 bandwidth and four times higher CXL bandwidth per port.

ADVANCED MICRO DEVICES, INC. (FORMERLY XILINX, INC.):

AMD offers products under four reportable segments: Data Center, Client, Gaming, and Embedded Segments. The Data Center segment offers CPUs, GPUs, FPGAs, DPUs, and adaptive SoC products for data centers. The portfolio of the Client segment consists of APUs, CPUs, and chipsets for desktop and notebook computers. The Gaming segment provides discrete GPUs, semi-custom SoC products, and development services. The Embedded segment offers embedded CPUs, GPUs, APUs, FPGAs, and Adaptive SoC devices. AMD offers its products to a wide range of industries, including aerospace & defense, architecture, engineering & construction, automotive, broadcast & professional audio/visual, government, consumer electronics, design & manufacturing, education, emulation & prototyping, healthcare & sciences, industrial & vision, media & entertainment, robotics, software & sciences, supercomputing & research, telecom & networking, test & measurement, and wired & wireless communications. AMD focuses on high-performance and adaptive computing technology, FPGAs, SoCs, and software.

Intel Corporation:Intel Corporation, based in the US, stands as one of the prominent manufacturers of semiconductor chips and various computing devices. The company's extensive product portfolio encompasses microprocessors, motherboard chipsets, network interface controllers, embedded processors, graphics chips, flash memory, and other devices related to computing and communications. Intel Corporation boasts substantial strengths in investment, marked by a long-standing commitment to research and development, a vast manufacturing infrastructure, and a robust focus on cutting-edge semiconductor technologies. For instance, in October 2023, Intel announced an expansion in Arizona that marked a significant milestone, underlining its dedication to meeting semiconductor demand, job creation, and advancing US technological leadership. Their dedication to expanding facilities and creating high-tech job opportunities is a testament to their strategic investments in innovation and growth.

[volersystems]

A leading aerospace company experienced this challenge head-on while developing a wearable night vision camera designed for military operations. With strict requirements for size, weight, power consumption, and performance, the company required a trustworthy partner with specialized expertise. Voler Systems, well-known for its innovation in FPGA design, electronic design, wearables, and firmware, collaborated to bring this ambitious project to life.

[riya2510]

5G Chipset to Witness Significant Growth by Forecast

Leading Forces in the 5G Chipset Market: Forecasts and Key Player Insights Through 2032

This Global 5G Chipset research report offers a comprehensive overview of the market, combining both qualitative and quantitative analyses. The qualitative analysis explores market dynamics such as growth drivers, challenges, and constraints, providing deep insights into the market's present and future potential. Meanwhile, the quantitative analysis presents historical and forecast data for key market segments, offering detailed statistical insights.

According to Straits Research, the global 5G Chipset market size was valued at USD 21 Billion in 2021. It is projected to reach from USD XX Billion in 2022 to USD 3170 Billion by 2030, growing at a CAGR of 87.2% during the forecast period (2022–2030).

Who are the leading companies (Marketing heads, regional heads) in the 5G Chipset 

Qualcomm Technologies Inc.

MediaTek Inc.

Samsung Electronics Co. Ltd

Xilinx Inc.

Broadcom Inc.

Infineon Technologies AG

Nokia Corporation

Huawei Technologies Co. Ltd

Renesas Electronics Corporation

Anokiwave Inc.

Qorvo Inc.

NXP Semiconductors NV

Intel Corporation

Cavium Inc.

Analog Devices Inc, Texas Instruments Inc.

We offer revenue share insights for the 5G Chipset Market, covering both publicly listed and privately held companies.

The report integrates comprehensive quantitative and qualitative analyses, offering a complete overview of the 5G Chipset. It spans from a macro-level examination of overall market size, industry chain, and market dynamics, to detailed micro-level insights into segment markets by type, application, and region. This approach provides a holistic view and deep understanding of the market, covering all critical aspects. Regarding the competitive landscape, the report highlights industry players, including market share, concentration ratios, and detailed profiles of leading companies. This enables readers to better understand their competitors and gain deeper insights into the competitive environment. Additionally, the report addresses key factors such as mergers and acquisitions, emerging market trends, the impact of COVID-19, and regional conflicts. In summary, this report is essential reading for industry players, investors, researchers, consultants, business strategists, and anyone with a stake or interest in entering the market.

Get Free Request Sample Report @ https://straitsresearch.com/report/5g-chipset-market/request-sample

The report integrates comprehensive quantitative and qualitative analyses, offering a complete overview of the 5G Chipset markets. It spans from a macro-level examination of overall market size, industry chain, and market dynamics, to detailed micro-level insights into segment markets by type, application, and region. This approach provides a holistic view and deep understanding of the market, covering all critical aspects. Regarding the competitive landscape, the report highlights industry players, including market share, concentration ratios, and detailed profiles of leading companies. This enables readers to better understand their competitors and gain deeper insights into the competitive environment. Additionally, the report addresses key factors such as mergers and acquisitions, emerging market trends, the impact of COVID-19, and regional conflicts. In summary, this report is essential reading for industry players, investors, researchers, consultants, business strategists, and anyone with a stake or interest in entering the market.

Global 5G Chipset Market: Segmentation

By Chipset Type

Application-specific Integrated Circuits (ASIC)

Radio Frequency Integrated Circuit (RFIC)

Millimeter Wave Technology Chips

Field-programmable Gate Array (FPGA)

By Operational Frequency

Sub-6 GHz

Between 26 and 39 GHz

Above 39 GHz

By End-User Industry

Consumer Electronics

Industrial Automation

Automotive and Transportation

Energy and Utilities

Healthcare

Retail

Other End-User Industries

Explore detailed Segmentation from here: @ https://straitsresearch.com/report/5g-chipset-market/segmentation

The report forecasts revenue growth at all geographic levels and provides an in-depth analysis of the latest industry trends and development patterns from 2022 to 2030 in each of the segments and sub-segments. Some of the major geographies included in the market are given below:

North America (U.S., Canada)

Europe (U.K., Germany, France, Italy)

Asia Pacific (China, India, Japan, Singapore, Malaysia)

Latin America (Brazil, Mexico)

Middle East & Africa

This Report is available for purchase on Buy 5G Chipset Market Report

Key Highlights

To explain 5G Chipset the following: introduction, product type and application, market overview, market analysis by countries, market opportunities, market risk, and market driving forces

The purpose of this study is to examine the manufacturers of 5G Chipset, including profile, primary business, news, sales and price, revenue, and market share.

To provide an overview of the competitive landscape among the leading manufacturers in the world, including sales, revenue, and market share of 5G Chipset percent

To illustrate the market subdivided by kind and application, complete with sales, price, revenue, market share, and growth rate broken down by type and application

To conduct an analysis of the main regions by manufacturers, categories, and applications, covering regions such as North America, Europe, Asia Pacific, the Middle East, and South America, with sales, revenue, and market share segmented by manufacturers, types, and applications.

To investigate the production costs, essential raw materials, production method, etc.

Buy Now @ https://straitsresearch.com/buy-now/5g-chipset-market

About Us:

StraitsResearch.com is a leading research and intelligence organization, specializing in research, analytics, and advisory services along with providing business insights & research reports.

Contact Us:

Email: sales@straitsresearch.com

Address: 825 3rd Avenue, New York, NY, USA, 10022

Tel: +1 6464807505, +44 203 318 2846

[aboutstraits]

Embedded Processor Market to have a high revenue growth rate over the next few years.

The 2024 Embedded Processor Market Report offers a comprehensive overview of the Embedded Processor Market industry, summarizing key findings on market size, growth projections, and major trends. It includes segmentation by region, by type, by product with targeted analysis for strategic guidance. The report also evaluates industry dynamics, highlighting growth drivers, challenges, and opportunities. Key stakeholders will benefit from the SWOT and PESTLE analyses, which provide insights into competitive strengths, vulnerabilities, opportunities, and threats across regions and industry segments. 

According to Straits Research, the global Embedded Processor Market  size was valued at USD 26.43 Billion in 2022. It is projected to reach from USD XX Billion in 2023 to USD 47.32 Billion by 2031, growing at a CAGR of 8.12% during the forecast period (2023–2031).

New Features in the 2024 Report:

Expanded Industry Overview: A more detailed and comprehensive examination of the industry.

In-Depth Company Profiles: Enhanced profiles offering extensive information on key market players.

Customized Reports and Analyst Assistance: Tailored reports and direct access to analyst support are available on request.

Embedded Processor Market Insights: Analysis of the latest market developments and upcoming growth opportunities.

Regional and Country-Specific Reports: Personalized reports focused on specific regions and countries to meet your unique requirements.

Detailed Table of Content of Embedded Processor Market report: @ https://straitsresearch.com/report/embedded-processor-market/toc

Report Structure

Economic Impact: Analysis of the economic effects on the industry.

Production and Opportunities: Examination of production processes, business opportunities, and potential.

Trends and Technologies: Overview of emerging trends, new technologies, and key industry players.

Cost and Market Analysis: Insights into manufacturing costs, marketing strategies, regional market shares, and market segmentation by type and application.

Request a free request sample (Full Report Starting from USD 995) : https://straitsresearch.com/report/embedded-processor-market/request-sample

Regional Analysis for Embedded Processor Market:

North America: The leading region in the Embedded Processor Market, driven by technological advancements, high consumer adoption rates, and favorable regulatory conditions. The United States and Canada are the main contributors to the region's robust growth.

Europe: Experiencing steady growth in the Embedded Processor Market, supported by stringent regulations, a strong focus on sustainability, and increased R&D investments. Key countries driving this growth include Germany, France, the United Kingdom, and Italy.

Asia-Pacific: The fastest-growing regional market, with significant growth due to rapid industrialization, urbanization, and a rising middle class. China, India, Japan, and South Korea are pivotal markets fueling this expansion.

Latin America, Middle East, and Africa: Emerging as growth regions for the Embedded Processor Market, with increasing demand driven by economic development and improved infrastructure. Key countries include Brazil and Mexico in Latin America, Saudi Arabia, the UAE, and South Africa in the Middle East and Africa.

Top Key Players of Embedded Processor Market :

NXP Semiconductors

Broadcom Corporation

STMicroelectronics

Intel Corporation

Infineon Technologies AG

Analog Devices Inc

Renesas Electronics

Microchip Technology Inc

Texas Instruments

ON Semiconductor

Embedded Processor Market Segmentations:

By Type

Microprocessor

Microcontrollers

Digital Signal Processor

Embedded FPGA

Others

By Application

Consumer Electronics

Automotive and Transportation

Industrial

Healthcare

IT & Telecom

Aerospace and Defense

Others

Get Detail Market Segmentation @ https://straitsresearch.com/report/embedded-processor-market/segmentation

FAQs answered in Embedded Processor Market Research Report

What recent brand-building initiatives have key players undertaken to enhance customer value in the Embedded Processor Market?

Which companies have broadened their focus by engaging in long-term societal initiatives?

Which firms have successfully navigated the challenges of the pandemic, and what strategies have they adopted to remain resilient?

What are the global trends in the Embedded Processor Market, and will demand increase or decrease in the coming years?

Where will strategic developments lead the industry in the mid to long term?

What factors influence the final price of Absorption Cooling Devices, and what raw materials are used in their manufacturing?

How significant is the growth opportunity for the Embedded Processor Market, and how will increasing adoption in mining affect the market's growth rate?

What recent industry trends can be leveraged to create additional revenue streams?

Scope

Impact of COVID-19: This section analyzes both the immediate and long-term effects of COVID-19 on the industry, offering insights into the current situation and future implications.

Industry Chain Analysis: Explores how the pandemic has disrupted the industry chain, with a focus on changes in marketing channels and supply chain dynamics.

Impact of the Middle East Crisis: Assesses the impact of the ongoing Middle East crisis on the market, examining its influence on industry stability, supply chains, and market trends.

This Report is available for purchase on @ https://straitsresearch.com/buy-now/embedded-processor-market

About Us:

Straits Research is a leading research and intelligence organization, specializing in research, analytics, and advisory services along with providing business insights & research reports.

Contact Us:

Email: sales@straitsresearch.com

Address: 825 3rd Avenue, New York, NY, USA, 10022

Tel: +1 646 905 0080 (U.S.) +91 8087085354 (India) +44 203 695 0070 (U.K.)

[govindhtech]

AMD Alveo UL3422 Accelerator Boots Electronic Trading Server

With the release of the fastest electronic trading accelerator in the world in a small form factor for widespread, affordable server deployments, AMD broadens its Alveo portfolio. The AMD Alveo UL3422 accelerator lowers entrance barriers while giving high-frequency traders an advantage in the competition for the quickest transaction execution.

The newest member of AMD’s record-breaking accelerator family for ultra-low latency electronic trading applications, the AMD Alveo UL3422 accelerator card, was unveiled today. The AMD Alveo UL3422 is a thin form factor accelerator that is geared for cost and rack space, and it is designed to be quickly deployed in a variety of servers for trading businesses, market makers, and financial institutions.

The AMD Virtex UltraScale+ FPGA, which powers the Alveo UL3422 accelerator, has a unique transceiver architecture with specialized, protected network connection components that are specifically designed for high-speed trading. By attaining less than 3ns FPGA transceiver latency and revolutionary “tick-to-trade” performance that is not possible with conventional off-the-shelf FPGAs, it makes ultra-low latency trade execution possible.

AMD Alveo UL3422 Accelerator

FinTech accelerator with the quickest trade execution in the world.

For economical deployment, the AMD Alveo UL3422 accelerator provides ultra-low latency (ULL) trading in a thin form factor.

Purpose-Built for ULL

Transceiver latency of less than 3 ns enables predictable, high-performance trade execution.

Slim Body Type

Economical implementation for widespread market acceptance and implementation in global exchanges.

Ease of Development

Ecosystem solutions and reference designs provide a quick route to commerce.

Key Features

Designed with Ultra-Low Latency (ULL) Trade Execution in Mind

Powered by Purpose-Built FPGA

Image Credit To AMD

With its exceptional ultra-low latency, the AMD Alveo UL3422 is the fastest trading accelerator in the world, giving traders the advantage they need to make decisions more quickly. The card has a cutting-edge transceiver architecture that achieves less than 3 ns latency for world-class trade execution, and it is powered by the AMD Virtex UltraScale+ VU2P FPGA designed for electronic trading.

Slim Form Factor for Cost-Effective Deployment in Diverse Servers in Any Exchange

Image Credit To AMD

The thin form factor of the AMD Alveo UL3422 accelerator allows for widespread adoption in a variety of server configurations, including Hypertec servers for instant deployment. Trading companies may efficiently use rack space co-located at market exchanges by using specially designed HFT equipment.

Ease of Development & Fast Path to Trade

FPGA Design Tools and Ecosystem Solutions

The Alveo UL3422 accelerator card, which has 1,680 DSP slices of computation and 780K LUTs of FPGA fabric, is designed to speed up proprietary trading algorithms in hardware so that traders may adapt their designs to new trade rules and changing trading algorithms. Using the Vivado Design Suite, conventional RTL development processes support the accelerator.

To activate the targeted Virtex UltraScale+ device, special license is needed. For license and access to more technical material, developers may apply for access to the Alveo UL3422 Secure Site. The GitHub Repository offers reference designs for testing various card functionalities and assessing latency and performance.

AMD also gives creators of low latency, AI-enabled trading algorithms the option to assess performance using the open source PyTorch-based framework (FINN). For quick trading algorithm installation, the card is also integrated with partner solution ecosystem partner solutions like Xelera Silva and Exegy nxFramework.

Fintech Applications

Competitive Advantage in Capital Markets

The Alveo UL3422 accelerator, which offers world-record performance, pre-trade risk management, market data delivery, and more, may be used by proprietary trading businesses, hedge funds, market makers, brokerages, and data providers for ULL algorithmic trading. High performance and determinism across a wide range of use cases are guaranteed by the combination of low latency networking, FPGA flexibility, and hardware acceleration.

Get Started

Start using the Alveo UL3422 accelerator card right now. accessible via authorized dealers and AMD.

Alveo UL3422 Accelerator Card

The AMD Alveo UL3422 is a thin-form-factor, ultra-low latency accelerator designed for affordable server deployment in exchanges throughout the globe. An AMD Virtex UltraScale+ FPGA designed specifically for electronic trading powers it. With its innovative transceiver design, the FPGA can execute world-record trades with latency of less than 3 ns, which is up to 7X lower than that of earlier AMD FPGA technologies.

Read more on Govindhtech.com

[microtroniks]

FPGA Video Overlay Solutions

FPGA video overlay solutions offer a versatile method for integrating graphics and video data seamlessly. These solutions enhance visual presentations by enabling the simultaneous display of multiple video sources. The technology supports various resolutions and formats, making it ideal for broadcast, live events, and gaming applications. With FPGA implementation, users can achieve high-performance processing with low latency, ensuring that overlays do not detract from the main content. Flexibility in design allows customization to meet specific project requirements. Microtronix stands out as a full-service product development company, providing hardware and software design services tailored to quickly get new products to the marketplace. Their 25 years of experience empower customers with cutting-edge networking communications and video products.

[vlsiguru24]

FPGA System Design Training - VLSI Guru

In the evolving world of digital design, FPGA System Design Training has become a cornerstone for engineers aspiring to excel in hardware design and embedded systems. VLSI Guru’s comprehensive FPGA System Design course equips you with the skills to design, develop, and implement FPGA-based systems using the latest industry tools and methodologies.

What is FPGA System Design?

FPGA (Field-Programmable Gate Array) System Design involves programming configurable logic blocks to create customized hardware solutions. FPGAs are widely used in applications like embedded systems, signal processing, and high-performance computing due to their flexibility, scalability, and speed.

Why Choose VLSI Guru for FPGA System Design Training?

1. Industry-Focused Curriculum

VLSI Guru’s training covers all essential aspects of FPGA System Design, including:

Basics of FPGA architecture and design flow.

Programming using VHDL and Verilog.

Advanced concepts like timing analysis, IP integration, and system optimization.

Hands-on experience with industry tools like Xilinx Vivado and Intel Quartus.

2. Hands-On Learning

Our training emphasizes practical exposure, allowing you to work on real-world FPGA projects such as:

Designing digital circuits.

Implementing communication protocols.

Developing hardware acceleration modules.

3. Expert Mentorship

Learn from experienced trainers with extensive industry expertise, ensuring you gain insights into real-world FPGA system design challenges and solutions.

4. Placement Assistance

VLSI Guru provides job-oriented training along with resume building, interview preparation, and placement support to help you secure roles in top hardware and semiconductor companies.

What Will You Learn?

Fundamentals of FPGA architecture and HDL programming.

Design, simulation, and implementation using Verilog/VHDL.

Prototyping on FPGA development boards.

Debugging and optimization techniques for FPGA-based systems.

Who Should Enroll?

This course is ideal for:

Engineering graduates in electronics, electrical, or related fields.

Freshers and professionals seeking a career in FPGA System Design.

Embedded system developers looking to enhance their hardware design skills.

Why FPGA System Design is in Demand

FPGAs are the backbone of modern electronics, powering innovations in telecommunications, automotive, IoT, and artificial intelligence. Skilled FPGA engineers are in high demand to meet the growing need for customized hardware solutions.

Join VLSI Guru Today

Get ahead in the competitive semiconductor industry with VLSI Guru’s FPGA System Design Training. Our hands-on approach, expert guidance, and career support ensure you are ready to tackle the challenges of FPGA-based system design.

Contact us now to learn more and take the first step toward an exciting career in FPGA design

[jcmarchi]

Moshe Tanach, CEO and Co-Founder at NeuReality – Interview Series

New Post has been published on https://thedigitalinsider.com/moshe-tanach-ceo-and-co-founder-at-neureality-interview-series/

Moshe Tanach, CEO and Co-Founder at NeuReality – Interview Series

Moshe Tanach is the CEO & co-founder of NeuReality. Before founding NeuReality, Moshe served as Director of Engineering at Marvell and Intel, where he led the development of complex wireless and networking products to mass production. He also served as AVP of R&D at DesignArt Networks (later acquired by Qualcomm), where he contributed to the development of 4G base station products.

NeuReality’s mission is to simplify AI adoption. By taking a system-level approach to AI, NeuReality’s team of industry experts delivers AI inference holistically, identifying pain points and providing purpose-built, silicon-to-software AI inference solutions that make AI both affordable and accessible.

With your extensive experience leading engineering projects at Marvell, Intel, and DesignArt-Networks, what inspired you to co-found NeuReality, and how did your previous roles influence the vision and direction of the company?

NeuReality was built from inception to solve for the future cost, complexity and climate problems that would be inevitable AI inferencing – which is the deployment of trained AI models and software into production-level AI data centers. Where AI training is how AI is created; AI inference is how it is used and how it interacts with billions of people and devices around the world.

We are a team of systems engineers, so we look at all angles, all the multiple facets of end-to-end AI inferencing including GPUs and all classes of purpose-built AI accelerators. It became clear to us going back to 2015 that CPU-reliant AI chips and systems – which is every GPU, TPU, LPU, NRU, ASIC and FPGA out there – would hit a significant wall by 2020. Its system limitations where the AI accelerator has become better and faster in terms of raw performance, but the underlying infrastructure did not keep up.

As a result, we decided to break away from the big giants riddled with bureaucracy that protect successful businesses, like CPU and NIC manufacturers, and disrupt the industry with a better AI architecture that is open, agnostic, and purpose-built for AI inference. One of the conclusions of reimagining ideal AI inference is that in boosting GPU utilization and system-level efficiency, our new AI compute and network infrastructure – powered by our novel NR1 server-on-chip that replaces the host CPU and NICs. As an ingredient brand and companion to any GPU or AI accelerator, we can remove market barriers that deter 65% of organizations from innovating and adopting AI today – underutilized GPUs which leads to buying more than what’s really needed (because they run idle > 50% of the time) – all the while reducing energy consumption, AI data center real-estate challenge, and operational costs.

This is a once in a lifetime opportunity to really transform AI system architecture for the better based on everything I learned and practiced for 30 years, opening the doors for new AI innovators across industries and removing CPU bottlenecks, complexity, and carbon footprints.

NeuReality’s mission is to democratize AI. Can you elaborate on what “AI for All” means to you and how NeuReality plans to achieve this vision?

Our mission is to democratize AI by making it more accessible and affordable to all organizations big and small – by unleashing the maximum capacity of any GPU or any AI accelerator so you get more from your investment; in other words, get MORE from the GPUs you buy, rather than buying more GPUs that run idle >50% of the time. We can boost AI accelerators up to 100% full capability, while delivering up to 15X energy-efficiency and slashing system costs by up to 90%. These are order of magnitude improvements. We plan to achieve this vision with our NR1 AI Inference Solution, the world’s first data center system architecture tailored for the AI age. It runs high-volume, high-variety AI data pipelines affordably and efficiently with the added benefit of a reduced carbon footprint.

Achieving AI for all also means making it easy to use. At NeuReality, we simplify AI infrastructure deployment, management, and scalability, enhance business processes and profitability, and advance sectors such as public health, safety, law enforcement and customer service. Our impact spans sectors such as medical imaging, clinical trials, fraud detection, AI content creation and many more.

Currently, our first commercially available NR1-S AI Inference Appliances are available with Qualcomm Cloud AI 100 Ultra accelerators and through Cirrascale, a cloud service provider.

The NR1 AI Inference Solution is touted as the first data center system architecture tailored for the AI age, and purpose-built for AI inference. What were the key innovations and breakthroughs that led to the development of the NR1?

NR1™ is the name of the entire silicon-to-software system architecture we’ve designed and delivered to the AI industry – as an open, fully compatible AI compute and networking infrastructure that fully complements any AI accelerator and GPUs. If I had to break it down to the top-most unique and exciting innovations that led to this end-to-end NR1 Solution and differentiates us, I’d say:

Optimized AI Compute Graphs: The team designed a Programmable Graph Execution Accelerator to optimize the processing of Compute Graphs, which are crucial for AI and various other workloads like media processing, databases, and more. Compute Graphs represent a series of operations with dependencies, and this broader applicability positions NR1 as potentially disruptive beyond just super boosting GPUs and other AI accelerators. It simplifies AI model deployment by generating optimized Compute Graphs (CGs) based on pre-processed AI data and software APIs, leading to significant performance gains.

NR1 NAPU™ (Network Addressable Processing Unit): Our AI inference architecture is powered by the NR1 NAPU™ – a 7nm server-on-chip that enables direct network access for AI pre- and post-processing. We pack 6.5x more punch on a smaller NR1 chip than a typical general-purpose, host CPU. Traditionally, pre-processing tasks (like data cleaning, formatting, and feature extraction) and post-processing tasks (like result interpretation and formatting) are handled by the CPU. By offloading these tasks to the NR1 NAPU™, we displace both the CPUs and NIC. This reduces bottlenecks allowing for faster overall processing, lightning-fast response times and lower cost per AI query. This reduces bottlenecks and allows for faster overall processing.

NR1™ AI-Hypervisor™ technology: The NR1’s patented hardware-based AI-Hypervisor™ optimizes AI task orchestration and resource utilization, improving efficiency and reducing bottlenecks.

NR1™ AI-over-Fabric™ Network Engine: The NR1 incorporates a unique AI-over-Fabric™ network engine that ensures seamless network connectivity and efficient scaling of AI resources across multiple NR1 chips – which are coupled with any GPU or AI Accelerator – within the same inference server or NR1-S AI inference appliance.

NeuReality’s recent performance data highlights significant cost and energy savings. Could you provide more details on how the NR1 achieves up to 90% cost savings and 15x better energy efficiency compared to traditional systems?

NeuReality’s NR1 slashes the cost and energy consumption of AI inference by up to 90% and 15x, respectively. This is achieved through:

Specialized Silicon: Our purpose-built AI inference infrastructure is powered by the NR1 NAPU™ server-on-chip, which absorbs the functionality of the CPU and NIC into one – and eliminates the need for CPUs in inference. Ultimately the NR1 maximizes the output of any AI accelerator or GPU in the most efficient way possible.

Optimized Architecture: By streamlining AI data flow and incorporating AI pre- and post-processing directly within the NR1 NAPU™, we offload and replace the CPU. This results in reduced latency, linear scalability, and lower cost per AI query.

Flexible Deployment: You can buy the NR1 in two primary ways: 1) inside the NR1-M™ Module which is a PCIe card that houses multiple NR1 NAPUs (typically 10) designed to pair with your existing AI accelerator cards. 2) inside the NR1-S™ Appliance, which pairs NR1 NAPUs with an equal number of AI accelerators (GPU, ASIC, FPGA, etc.) as a ready-to-go AI Inference system.

At Supercomputing 2024 in November, you will see us demonstrate an NR1-S Appliance with 4x NR1 chips per 16x Qualcomm Cloud AI 100 Ultra accelerators. We’ve tested the same with Nvidia AI inference chips. NeuReality is revolutionizing AI inference with its open, purpose-built architecture.

 How does the NR1-S AI Inference Appliance match up with Qualcomm® Cloud AI 100 accelerators compare against traditional CPU-centric inference servers with Nvidia® H100 or L40S GPUs in real-world applications?

NR1, combined with Qualcomm Cloud AI 100 or NVIDIA H100 or L40S GPUs, delivers a substantial performance boost over traditional CPU-centric inference servers in real-world AI applications across large language models like Llama 3, computer vision, natural language processing and speech recognition. In other words, running your AI inference system with NR1 optimizes the performance, system cost, energy efficiency and response times across images, sound, language, and text – both separately (single modality) or together (multi-modality).

The end-result? When paired with NR1, a customer gets MORE from the expensive GPU investments they make, rather than BUYING more GPUs to achieve desired performance.

Beyond maximizing GPU utilization, the NR1 delivers exceptional efficiency, resulting in 50-90% better price/performance and up to 13-15x greater energy efficiency. This translates to significant cost savings and a reduced environmental footprint for your AI infrastructure.

The NR1-S demonstrates linear scalability with no performance drop-offs. Can you explain the technical aspects that allow such seamless scalability?

The NR1-S Appliance, coupling our NR1 chips with AI accelerators of any type or quantity, redefines AI infrastructure. We’ve moved beyond CPU-centric limitations to achieve a new level of performance and efficiency.

Instead of the traditional NIC-to-CPU-to-accelerator bottleneck, the NR1-S integrates direct network access, AI pre-processing, and post-processing within our Network Addressable Processing Units (NAPUs). With typically 10 NAPUs per system, each handling tasks like vision, audio, and DSP processing, and our AI-Hypervisor™ orchestrating workloads, streamlined AI data flow is achieved. This translates to linear scalability: add more accelerators, get proportionally more performance.

The result? 100% utilization of AI accelerators is consistently observed. While overall cost and energy efficiency vary depending on the specific AI chips used, maximized hardware investment, and improved performance are consistently delivered. As AI inference needs scale, the NR1-S provides a compelling alternative to traditional architectures.

NeuReality aims to address the barriers to widespread AI adoption. What are the most significant challenges businesses face when adopting AI, and how does your technology help overcome these?

When poorly implemented, AI software and solutions can become troublesome. Many businesses cannot adopt AI due to the cost and complexity of building and scaling AI systems. Today’s AI solutions are not optimized for inference, with training pods typically having poor efficiency and inference servers having high bottlenecks. To take on this challenge and make AI more accessible, we have developed the first complete AI inference solution – a compute and networking infrastructure powered by our NAPU – which makes the most of its companion AI accelerator and reduces market barriers around excessive cost and energy consumption.

Our system-level approach to AI inference – versus trying to develop a better GPU or AI accelerator where there is already a lot of innovation and competition – means we are filling a significant industry gap for dozens of AI inference chip and system innovators. Our team attacked the shortcomings in AI Inference systemically and holistically, by determining pain points, architecture gaps and AI workload projections — to deliver the first purpose-built, silicon-to-software, CPU-free AI inference architecture. And by developing a top-to-bottom AI software stack with open standards from Python and Kubernetes combined with NeuReality Toolchain, Provisioning, and Inference APIs, our integrated set of software tools combines all components into a single high-quality UI/UX.

In a competitive AI market, what sets NeuReality apart from other AI inference solution providers?

To put it simply, we’re open and accelerator-agnostic. Our NR1 inference infrastructure supercharges any AI accelerator – GPU, TPU, LPU, ASIC, you name it – creating a truly optimized end-to-end system. AI accelerators were initially brought in to help CPUs handle the demands of neural networks and machine learning at large, but now the AI accelerators have become so powerful, they’re now held back by the very CPUs they were meant to assist.

Our solution? The NR1. It’s a complete, reimagined AI inference architecture. Our secret weapon? The NR1 NAPU™ was designed as a co-ingredient to maximize AI accelerator performance without guzzling extra power or breaking the bank. We’ve built an open ecosystem, seamlessly integrating with any AI inference chip and popular software frameworks like Kubernetes, Python, TensorFlow, and more.

NeuReality’s open approach means we’re not competing with the AI landscape; we’re here to complement it through strategic partnerships and technology collaboration. We provide the missing piece of the puzzle: a purpose-built, CPU-free inference architecture that not only unlocks AI accelerators to benchmark performance, but also makes it easier for businesses and governments to adopt AI. Imagine unleashing the full power of NVIDIA H100s, Google TPUs, or AMD MI300s – giving them the infrastructure they deserve.

NeuReality’s open, efficient architecture levels the playing field, making AI more accessible and affordable for everyone. I’m passionate about seeing different industries – fintech, biotech, healthtech – experience the NR1 advantage firsthand. Compare your AI solutions on traditional CPU-bound systems versus the modern NR1 infrastructure and witness the difference. Today, only 35% of businesses and governments have adopted AI and that is based on incredibly low qualifying criteria. Let’s make it possible for over 50% of enterprise customers to adopt AI by this time next year without harming the planet or breaking the bank.

Looking ahead, what is NeuReality’s long-term vision for the role of AI in society, and how do you see your company contributing to this future?

I envision a future where AI benefits everyone, fostering innovation and improving lives. We’re not just building technology; we’re building the foundation for a better future.

Our NR1 is key to that vision. It’s a complete AI inference solution that starts to shatter the cost and complexity barriers hindering mass AI business adoption. We’ve reimagined both the infrastructure and the architecture, delivering a revolutionary system that maximizes the output of any GPU, any AI accelerator, without increasing operational costs or energy consumption.

The business model really matters to scale and give end-customers real choices over concentrated AI autocracy as I’ve written on before. So instead, we’re building an open ecosystem where our silicon works with other silicon, not against it. That’s why we designed NR1 to integrate seamlessly with all AI accelerators and with open models and software, making it as easy as possible to install, manage and scale.

But we’re not stopping there. We’re collaborating with partners to validate our technology across various AI workloads and deliver “inference-as-a-service” and “LLM-as-a-service” through cloud service providers, hyper scalers, and directly with companion chip makers. We want to make advanced AI accessible and affordable to all.

Imagine the possibilities if we could boost AI inference performance, energy efficiency, and affordability by double-digit percentages. Imagine a robust, AI-enabled society with more voices and choices becoming a reality. So, we must all do the demanding work of proving business impact and ROI when AI is implemented in daily data center operations. Let’s focus on revolutionary AI implementation, not just AI model capability.

This is how we contribute to a future where AI benefits everyone – a win for profit margins, people, and the planet.

Thank you for the great interview, readers who wish to learn more should visit NeuReality.