Sources familiar with the matter disclosed to Reuters that OpenAI is actively exploring options, including evaluating an undisclosed company for potential acquisition to bolster its AI chip-making ambitions.

The shortage of chips, a fundamental component in AI technology, has prompted OpenAI to consider various strategies. These options include internal chip production, forging closer ties with its primary chip supplier NVIDIA, and diversifying its chip providers.

Earlier this year, OpenAI CEO Sam Altman voiced his concerns about the chip scarcity—resulting in delays to the company’s projects.

In a since-deleted blog post by Humanloop CEO Raza Habib, the AI expert wrote about his experience sitting down with Altman:

“A common theme that came up throughout the discussion was that currently OpenAI is extremely GPU-limited and this is delaying a lot of their short-term plans. The biggest customer complaint was about the reliability and speed of the API.

Sam acknowledged their concern and explained that most of the issue was a result of GPU shortages.The longer 32k context can’t yet be rolled out to more people. OpenAI haven’t overcome the O(n^2) scaling of attention and so whilst it seemed plausible they would have 100k – 1M token context windows soon (this year) anything bigger would require a research breakthrough.

The finetuning API is also currently bottlenecked by GPU availability. They don’t yet use efficient finetuning methods like Adapters or LoRa and so finetuning is very compute-intensive to run and manage.

Better support for finetuning will come in the future. They may even host a marketplace of community contributed models. Dedicated capacity offering is limited by GPU availability.”

If OpenAI proceeds with its plan to manufacture its own chips, it will join the ranks of industry giants like Google and Amazon who have already transitioned to in-house chip production. This move could potentially alleviate OpenAI’s dependency on external suppliers, empowering the company to meet the escalating demand for specialised AI chips.

Since the public launch of ChatGPT in November last year, the demand for specialised AI chips has skyrocketed—causing a surge in NVIDIA’s share prices as companies rush to procure the desirable hardware.

OpenAI has not made a final decision regarding the acquisition or in-house chip production, and discussions are ongoing to address the pressing chip shortage and sustain the company’s AI initiatives.

(Photo by Andrew Neel on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post OpenAI considers in-house chip manufacturing amid global shortage appeared first on AI News.

Omdia’s Top AI Hardware Startups Market Radar finds that over 100 venture capitalists invested over $6 billion into the top 25 AI chip startups since 2018. However, it seems the good times weren’t to last.

The global chip shortage is becoming an inventory crisis. Meanwhile, the economic downturn and difficult monetary policies have made it difficult to raise funding.

“The best-funded AI chip startups are under pressure to deliver the kind of software support developers are used to from the market leader, NVIDIA,” says Alexander Harrowell, Principal Analyst for Advanced Computing at Omdia.

“This is the key barrier to getting new AI chip technology into the market.”

Omdia predicts that at least one major startup will exit the market this year, likely through a sale to a major chipmaker or a hyperscale cloud provider.

“The most likely exit route is probably via trade sales to major vendors,” adds Harrowell.

“Apple has $23 billion in cash on its balance sheet and Amazon $35 billion, while Intel, NVIDIA, and AMD have some $10 billion between them. The hyperscalers have been very keen to adopt custom AI silicon and they can afford to maintain the skills involved.”

Over half of the $6 billion invested in AI chip startups have focused on large-die, CGRA  accelerators that are designed with the aim of loading entire AI models on-chip. That approach is now being questioned due to the continuing growth of AI models.

“In 2018 and 2019, the idea of bringing the entire model into on-chip memory made sense, as this approach offers extremely low latency and answers the input/output problems of large AI models,” explains Harrowell.

“However, the models have continued to grow dramatically ever since, making scalability a critical issue. More structured and internally complex models mean AI processors must offer more general-purpose programmability. As such, the future of AI processors may lie in a different direction.”

(Photo by Fabrizio Conti on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Omdia: AI chip startups to have a tough year appeared first on AI News.

In an SEC filing, NVIDIA says the US government has informed the chipmaker of a new license requirement that impacts two of its GPUs designed to speed up machine learning tasks: the current A100, and the upcoming H100.

“The license requirement also includes any future NVIDIA integrated circuit achieving both peak performance and chip-to-chip I/O performance equal to or greater than thresholds that are roughly equivalent to the A100, as well as any system that includes those circuits,” adds NVIDIA.

The US government has reportedly told NVIDIA that the new rules are geared at addressing the risk of the affected products being used for military purposes.

“While we are not in a position to outline specific policy changes at this time, we are taking a comprehensive approach to implement additional actions necessary related to technologies, end-uses, and end-users to protect US national security and foreign policy interests,” said a US Department of Commerce spokesperson.

China is a large market for NVIDIA and the new rules could affect around $400 million in quarterly sales.

AMD has also been told the new rules will impact its similar products, including the MI200.

As of writing, NVIDIA’s shares were down 11.45 percent from the market open. AMD’s shares are down 6.81 percent. However, it’s worth noting that it’s been another red day for the wider stock market.

(Photo by Wesley Tingey on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post US introduces new AI chip export restrictions appeared first on AI News.

The Chinese venture of the British semiconductor icon began operating as an independent company and conducted its own in-house R&D to create new IP. Dylan Patel, Chief Analyst at SemiAnalysis, even penned a piece titled: ‘The Semiconductor Heist Of The Century – Arm China Has Gone Completely Rogue’.

Arm-owner SoftBank sold 51 percent of its stake in the Chinese venture, Arm Limited, to a consortium of Chinese investors for $775 million. With its remaining stake, SoftBank no longer had a majority to make any major decisions.

Arm China fired its CEO, Allen Wu, in June 2020 after he was accused of offering discounts to customers if they invested in his side hustle, Alphatecture. However, Wu refused to leave arguing that: “Arm China did not convene any valid board meeting”.

What followed was lawsuits to oust Wu from his post. In the meantime, Wu reportedly got rid of staff loyal to Arm from Arm China and even employed security guards in a bid to keep out unwanted guests to retain his position.

However, Nikkei and Reuters have reported that Wu has now been removed.

SoftBank will be pleased with the news as the certainty it provides will make it easier for the company to launch an IPO of Arm.

Arm is set to launch an IPO after the collapse of a $40 billion acquisition offer from Nvidia. The deal collapsed following scrutiny from numerous global regulators that were concerned Nvidia could limit rivals’ access to Arm’s chip designs or shift resources towards areas that benefit its new owner.

SoftBank considered and subsequently rejected the idea of pursuing an IPO (Initial Public Offering) of the company in 2019 and again in early 2020.

“We contemplated an IPO but determined that the pressure to deliver short-term revenue growth and profitability would suffocate our ability to invest, expand, move fast, and innovate,” explained Simon Segars, CEO of Arm, in January.

The company’s hand is now being somewhat forced through a lack of alternative options.

Arm has struggled from relatively flat revenues and rising costs despite the huge success of the company’s licensees such as Apple, Qualcomm, and Amazon. However, SoftBank has been keen to hype the company’s future prospects.

“Arm is becoming a centre of innovation not only in the mobile phone revolution, but also in cloud computing, automotive, the Internet of Things, and the metaverse, and has entered its second growth phase,” said Masayoshi Son, Representative Director, Corporate Officer, Chairman, and CEO of SoftBank Group.

In March, Arm announced that it was cutting up to 1,000 jobs from its global workforce. The move was seen as a bid to show potential investors that it’s running a leaner operation.

“To stay competitive, we need to remove duplication of work now that we are one Arm; stop work that is no longer critical to our future success; and think about how we get work done,” wrote Arm CEO Rene Haas in an email to staff.

Haas, the former head of Arm’s intellectual property unit, recently took over as the company’s chief executive as part of its internal strategy shakeup to help navigate it through these choppy waters.

(Photo by Laurent Perren on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Arm takes back control of its Chinese biz ahead of IPO appeared first on AI News.

“A sharp focus on AI hardware manufacturing, research, and design is critical to meet the demands of the world’s evolving devices, architectures, and systems,” says Anantha Chandrakasan, dean of the MIT School of Engineering, and Vannevar Bush Professor of Electrical Engineering and Computer Science. 

“Knowledge-sharing between industry and academia is imperative to the future of high-performance computing.”

There are five inaugural members of the program:

• Amazon
• Analog Devices
• ASML
• NTT Research
• TSMC

As the diversity of the inaugural members shows, the program is intended to be a cross-disciplinary effort.

“As AI systems become more sophisticated, new solutions are sorely needed to enable more advanced applications and deliver greater performance,” commented Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and Henry Ellis Warren Professor of Electrical Engineering and Computer Science

 “Our aim is to devise real-world technological solutions and lead the development of technologies for AI in hardware and software.”

A key goal of the program is to help create more energy-efficient systems.

“We are all in awe at the seemingly superhuman capabilities of today’s AI systems. But this comes at a rapidly increasing and unsustainable energy cost,” explained Jesús del Alamo, the Donner Professor in MIT’s Department of Electrical Engineering and Computer Science.

“Continued progress in AI will require new and vastly more energy-efficient systems. This, in turn, will demand innovations across the entire abstraction stack, from materials and devices to systems and software. The program is in a unique position to contribute to this quest.”

Other key areas of exploration include:

• Analog neural networks
• New CMOS designs
• Heterogeneous integration for AI systems
• Monolithic-3D AI systems
• Analog nonvolatile memory devices
• Software-hardware co-design
• Intelligence at the edge
• Intelligent sensors
• Energy-efficient AI
• Intelligent Internet of Things (IIoT)
• Neuromorphic computing
• AI edge security
• Quantum AI
• Wireless technologies
• Hybrid-cloud computing
• High-performance computation

It’s an exhaustive list and an ambitious project. However, the AI Hardware Program is off to a great start with the inaugural members bringing significant talent and expertise in their respective fields to the table.

“We live in an era where paradigm-shifting discoveries in hardware, systems communications, and computing have become mandatory to find sustainable solutions—solutions that we are proud to give to the world and generations to come,” says Aude Oliva, Senior Research Scientist in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and Director of Strategic Industry Engagement at the MIT Schwarzman College of Computing.

The program is being co-led by Jesús del Alamo and Aude Oliva. Anantha Chandrakasan will serve as its chair.

More information about the AI Hardware Program can be found here.

(Photo by Nejc Soklič on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post MIT launches cross-disciplinary program to boost AI hardware innovation appeared first on AI News.

The British chip designer has struggled from relatively flat revenues and rising costs despite the huge success of the company’s licensees such as Apple, Qualcomm, and Amazon.

Nvidia proposed to acquire Arm for $40 billion and ensure the company is able to continue its pioneering work. However, the deal caught the eye of global regulators and rivals voiced their concerns that Nvidia could limit their access to Arm’s chip designs or shift resources towards areas that benefit its new owner.

Despite Nvidia offering legal assurances to counter the concerns, the deal looked set to be blocked by regulators. Last month, Nvidia decided to throw in the towel.

“Though we won’t be one company, we will partner closely with Arm,” said Nvidia CEO Jensen Huang at the time.

“The significant investments that Masa has made have positioned Arm to expand the reach of the Arm CPU beyond client computing to supercomputing, cloud, AI, and robotics.”

Arm was always now going to face some tough decisions. An IPO (Initial Public Offering) is the most likely outcome but is far from ideal.

SoftBank, Arm’s current owner, considered and subsequently rejected the idea of pursuing an IPO (Initial Public Offering) of the company in 2019 and again in early 2020.

“We contemplated an IPO but determined that the pressure to deliver short-term revenue growth and profitability would suffocate our ability to invest, expand, move fast, and innovate,” explained Simon Segars, CEO of Arm, in January.

However, faced with limited options, the IPO route now look set. Arm’s decision to cut its workforce looks to be part of a bid to boost potential investors’ confidence that a more lean operation with fewer overheads will improve rather than hinder the company’s prospects.

“To stay competitive, we need to remove duplication of work now that we are one Arm; stop work that is no longer critical to our future success; and think about how we get work done,” wrote Arm CEO Rene Haas in an email to staff.

Haas, the former head of Arm’s intellectual property unit, recently took over as the company’s chief executive as part of its internal strategy shakeup to help navigate it through these choppy waters.

Arm’s appeal is the company’s technical expertise in a world where such talent is a hot commodity. The company will need to convince investors the cuts won’t impact Arm’s groundbreaking work.

SoftBank is obviously keen to put a bullish outlook on Arm’s future.

“Arm is becoming a centre of innovation not only in the mobile phone revolution, but also in cloud computing, automotive, the Internet of Things, and the metaverse, and has entered its second growth phase,” said Masayoshi Son, Representative Director, Corporate Officer, Chairman, and CEO of SoftBank Group.

Whatever happens, we hope Arm finds a way to continue delivering the innovation that it has for the past three decades.

(Photo by Matt Artz on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Arm is cutting up to 1,000 jobs after Nvidia deal collapses appeared first on AI News.

The chip unveiled today, the Bow IPU, is the world’s first processor to be based on TSMC’s Wafer-on-Wafer (WoW) technology.

“TSMC has worked closely with Graphcore as a leading customer for our breakthrough SoIC-WoW solution as their pioneering designs in cutting-edge parallel processing architectures make them an ideal match for our technology,” said Paul de Bot, GM of TSMC Europe. 

WoW bonds two wafers together to generate a new 3D die:

• The first wafer is for AI processing and is architecturally compatible with the GC200 IPU processor. It has 1,472 independent IPU-Core tiles, is capable of running more than 8,800 threads, and has 900MB of in-processor memory.
• The second wafer is a power delivery die and features deep trench capacitors that enable a large performance increase thanks to being located right next to the processing cores and memory.

Compared to its predecessors, Graphcore claims that its Bow IPU offers up to 40 percent higher performance and 16 percent better power efficiency for real-world AI applications.

In terms of power, Graphcore says the flagship Bow Pod delivers more than 89 petaFLOPS of AI compute. The superscale Bow POD ups that to an incredible 350 petaFLOPS.

Here’s how that power translates into real-world performance across popular AI applications:

Ultra-intelligence AI supercomputer

The most interesting announcement made by Graphcore today is of its plan to develop an “ultra-intelligence AI supercomputer”.

Graphcore points to how the human brain has “approximately 100 billion neurons and more than 100 trillion parameters in a biological-neural-network system that delivers a level of compute yet to be matched by any silicon computers.”

Graphcore says that it’s developing an AI computer that will surpass the parametric capacity of the brain.

The computer will be called the ‘Good’ computer, named after computer science pioneer Jack Good (born Isadore Jacob Gudak).

The achievements of Jack Good – including his pivotal work during the Second World War – are worth reading up on. However, for the purposes of this story, what’s most notable is the fact Good was the first person to describe a machine that is more powerful than a human brain in his 1965 paper Speculations Concerning the First Ultra-Intelligent Machine.

“Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever,” wrote Good.

“Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make.”

By 2024, Graphcore expects to have delivered the first ultra-intelligence AI computer.

The computer will feature:

• Over 10 exaFLOPS of AI floating point compute
• Up to four petabytes of memory with a bandwidth of over 10 petabytes/second
• Support for AI model sizes of 500 trillion parameters 
• 3D wafer on wafer logic stack
• Fully supported by Graphcore’s Poplar® SDK 
• Expected cost: ~$120 million (configuration dependent) 

Graphcore says it will provide further updates about the Good computer over the coming year.

(Imagery Credit: Graphcore)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Graphcore unveils the first WoW processor alongside ‘ultra-intelligence AI supercomputer’ plans appeared first on AI News.

The chip, the AML100, is the industry’s first analog tiny machine learning solution. In practice, that means always-on system power is reduced by 95 percent.

Key features:

• Consumes less than 20µA when always-sensing
• Intelligently reduces quantity of data by up to 100x while the data are still in analog
• Features field-programmable functionality to address a wide range of always-on applications
• Leverages patented analog compression technology for preroll collection to maintain accuracy of wake word engine in voice-enabled devices
• Supports 4 analog sensors in any combination (microphones, accelerometers, etc.)
• Available in 7mm x 7mm 48-pin QFN package

Devices that previously required a wired power connection – or large battery, where viable – can use the AML100 to create new product classes and/or enable more flexible deployments.

Tom Doyle, Founder and CEO of Aspinity, said:

“We’ve long realised that reducing the power of each individual chip within an always-on system provides only incremental improvements to battery life. That’s not good enough for manufacturers who need revolutionary power improvements.

The AML100 reduces always-on system power to under 100µA, and that unlocks the potential of thousands of new kinds of applications running on battery.”

Current always-on devices continuously collect vast amounts of natively analog data and therefore consume a large amount of power to process mostly irrelevant data.

Aspinity claims the AML100 moves the machine learning workload to ultra-low-power analog “where the AML100 can determine data relevancy with a high degree of accuracy and at near-zero power.”

The AML100 is set for mass production in Q4 2022.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Aspinity unveils the first analog machine learning chip appeared first on AI News.

The deal caught the attention of global regulators with anti-competition investigations launched in several jurisdictions including the UK, EU, and US.

In November 2021, UK Digital Secretary Nadine Dorries decided to block the merger pending the results of a 24-week ‘Phase 2’ investigation.

With the merger looking almost impossible to be approved by regulators, Nvidia has decided to throw in the towel.

Jensen Huang, Founder and CEO of Nvidia, said:

“Arm has a bright future, and we’ll continue to support them as a proud licensee for decades to come.

Arm is at the centre of the important dynamics in computing. Though we won’t be one company, we will partner closely with Arm.

The significant investments that Masa has made have positioned Arm to expand the reach of the Arm CPU beyond client computing to supercomputing, cloud, AI, and robotics.

I expect Arm to be the most important CPU architecture of the next decade.”

Arm has struggled from relatively flat revenues and rising costs despite the huge success of the company’s licensees such as Apple, Qualcomm, and Amazon.

SoftBank, Arm’s current owner, considered and subsequently rejected the idea of pursuing an IPO (Initial Public Offering) of the company in 2019 and again in early 2020.

“We contemplated an IPO but determined that the pressure to deliver short-term revenue growth and profitability would suffocate our ability to invest, expand, move fast, and innovate,” explained Simon Segars, CEO of Arm, last month.

Following the collapse of the Nvidia acquisition, Softbank will now have to reconsider an IPO for Arm.

Dr Lil Read, Analyst in the Thematic Research Team at GlobalData, commented:

“Softbank now needs to think of Arm’s future. An initial public offering (IPO) looks likely – the UK government would surely like to see the home-grown chip designer float in London, and potential IPO reforms could create the perfect environment for this. 

Otherwise, Arm may be ripe for a takeover by a private equity consortium backed by chip-friendly giants such as Apple, Qualcomm, and TSMC – Arm’s largest customers.”

Some of Nvidia’s rivals are said to have offered to invest in Arm if it helps the company to remain independent. A takeover from a private equity consortium looks to be Arm’s best option. If the company has to launch an IPO, it could struggle and will face some difficult choices.

Arm’s largest market, mobile, is saturated. The company will struggle to crack the datacentre and PC markets in the face of strong incumbents like Intel and AMD that have established ecosystem of developers, software, systems, and peripherals, and profits that enable them to make large R&D investments.

In an earlier response to the UK’s Competition and Markets Authority, aiming to quell the regulator’s fears about its acquisition of Arm, Nvidia wrote:

“Nvidia is particularly concerned that these pressures would drive Arm to deprioritize datacenter and PC and to instead focus on its core mobile and growing IoT businesses.

The result would be a concentrated CPU market largely controlled by Intel/AMD (x86).”

Capital markets would likely expect Arm to cut costs to maximise the company’s value. However, SoftBank sounds bullish on its prospects.

“Arm is becoming a centre of innovation not only in the mobile phone revolution, but also in cloud computing, automotive, the Internet of Things, and the metaverse, and has entered its second growth phase,” said Masayoshi Son, Representative Director, Corporate Officer, Chairman, and CEO of SoftBank Group.

Arm has announced a management shake-up in the wake of Nvidia’s exit from the deal.

Rene Haas, the former head of Arm’s intellectual property unit, will take over as the company’s chief executive and lead it during these challenging times. Haas previously worked at Nvidia for seven years.

With the Nvidia acquisition off the table, we can only hope that Haas finds a way to ensure Arm can continue to deliver the semiconductor innovation that it has for three decades.

(Photo by Dustin Tramel on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Nvidia exits from its proposed $40B acquisition of Arm appeared first on AI News.

Nvidia announced plans to acquire Cambridge-based Arm back in September 2020 in a deal worth $40 billion. As two of the biggest names in chip manufacturing, the deal naturally caught the attention of competition regulators around the world.

In November 2021, UK Digital Secretary Nadine Dorries decided to block the merger pending the results of a 24-week ‘Phase 2’ investigation.

Wouldn’t an IPO be an alternative to all of this?

Arm has been struggling from relatively flat revenues and rising costs despite the huge success of the company’s licensees such as Apple, Qualcomm, and Amazon. Arm’s current owner, SoftBank, considered and subsequently rejected the idea of pursuing an IPO (Initial Public Offering) of the company in 2019 and again in early 2020.

Simon Segars, CEO of Arm, explained: “We contemplated an IPO but determined that the pressure to deliver short-term revenue growth and profitability would suffocate our ability to invest, expand, move fast, and innovate.”

In addition, Arm’s largest market, mobile, is saturated and it could be difficult for Arm to crack the datacentre and PC markets in the face of strong incumbents like Intel and AMD that have established ecosystem of developers, software, systems, and peripherals, and profits that enable them to make large R&D investments.

“These observations are not criticisms of Arm’s technology or engineering team. Arm has great engineering talent in the areas where it focuses. But as a standalone IP licensing business, and without access to further capital, Arm has inherent scale, scope, and economic limitations that would impact Arm’s future as a standalone licensing firm,” reads the response.

Addressing competition concerns

Nvidia believes that its acquisition would provide Arm with the resources to move forward in the face of limited options. Capital markets. meanwhile, would expect Arm to cut costs to maximise the company’s value.

“Nvidia is particularly concerned that these pressures would drive Arm to deprioritize datacenter and PC and to instead focus on its core mobile and growing IoT businesses. The result would be a concentrated CPU market largely controlled by Intel/AMD (x86),” the response continues.

The company goes on to say that soaring profits for Arm’s customers are not a win for the company or for competition. Nvidia argues the “industry titans will be powerful and competitive, no matter what path Arm takes” and “the question at hand is whether regulators will approve the transaction and allow Arm to take the steps needed to enable others to compete.”

Nvidia highlights that it was SoftBank that approached the company about the potential of acquiring Arm rather than the other way around. Nvidia says it will continue to support x86 as it has a vested interest – building platforms such as Omniverse on such systems – but thinks that it can help Arm build a viable alternative ecosystem that will increase options and encourage the x86 giants to innovate.

Arm’s future as a British tech icon

From the UK’s perspective, another major concern was the impact on jobs and even the company’s future in the country. Hermann Hauser, the founder of Arm, once suggested the acquisition would be “surrendering the UK’s most powerful trade weapon to the US”.

In a binding offer, Nvidia committed to expanding Arm’s engineering teams in the UK—including a pledge to create a new group dedicated to creating purpose-built CPU IP for datacentres and PCs.

Nvidia chose to build its Cambridge-1 supercomputer in the UK, which was seen by many as a bid to show its commitment to the country. The firm also opened a new AI centre in Cambridge—home to an increasing number of exciting startups in the field such as FiveAI, Prowler.io, Fetch.ai, and Darktrace.

“We will create an open centre of excellence in the area once home to giants like Isaac Newton and Alan Turing, for whom key Nvidia technologies are named,” said Nvidia CEO Jensen Huang at the time.

“Here, leading scientists, engineers and researchers from the UK and around the world will come to develop their ideas, collaborate and conduct their ground-breaking work in areas like healthcare, life sciences, self-driving cars, and other fields.”

No incentive to foreclose

The final major concern around the acquisition is that the merged entities will refuse or restrict the licensing of future IP to give Nvidia a competitive advantage.

Nvidia points to the emergence of RISC-V and other alternatives to Arm that could become preferable “if the Merged Entity were to refuse to license future datacenter IP as soon as it can”. However, it says that would be many years down the road as “customers already have the IP in their possession, and it will be many years before their contracts are up for renewal”.

Furthermore, Nvidia says that it would have no economic incentive to foreclose:

“NVIDIA knows that such a strategy would be self-defeating, and has no incentive to pursue it. The Decision does not explain why any downstream customer would embrace a sole-source ecosystem. Even x86 has always had two bona fide suppliers (Intel and AMD), and now, x86 is licensable to anyone.”

It also says that Arm’s customers are Nvidia’s customers and any attempt to foreclose would damage its own business and reputation.

While regulators have been mulling whether or not to approve the deal, Nvidia says that Arm’s competition has been exploiting the delay to continue expanding their offerings.

Nvidia makes some fairly solid arguments in its response, but whether they’re enough to woo regulators is another question. It’s not just the UK’s regulator examining the deal, but also respective agencies from the US, China, and EU.

(Photo by Mika Baumeister on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Nvidia attempts to alleviate Arm merger concerns in CMA response appeared first on AI News.