Recently, Arm unveiled new chip blueprints and software tools aimed at enhancing smartphones’ ability to handle AI tasks more efficiently. But they didn’t stop there – Arm also implemented changes to how they deliver these blueprints, potentially accelerating adoption.

Arm is evolving its solution offerings to maximise the benefits of leading process nodes. They announced the Arm Compute Subsystems (CSS) for Client, their latest cutting-edge compute solution tailored for AI applications in smartphones and PCs.

This CSS for Client promises a significant performance leap – we’re talking over 30% increased compute and graphics performance, along with an impressive 59% faster AI inference for AI, machine learning, and computer vision workloads.

While Arm’s technology powered the smartphone revolution, it’s also gaining traction in PCs and data centres, where energy efficiency is prized. Though smartphones remain Arm’s biggest market, supplying IP to rivals like Apple, Qualcomm, and MediaTek, the company is expanding its offerings.

They’ve launched new CPU designs optimised for AI workloads and new GPUs, as well as software tools to ease the development of chatbots and other AI apps on Arm chips.

But the real gamechanger is how these products are delivered. Historically, Arm provided specs or abstract designs that chipmakers had to translate into physical blueprints – an immense challenge arranging billions of transistors.

For this latest offering, Arm collaborated with Samsung and TSMC to provide physical chip blueprints ready for manufacturing, which was a huge time saver.

Samsung’s Jongwook Kye praised the partnership, stating their 3nm process combined with Arm’s CPU solutions meets soaring demand for generative AI in mobiles through “early and tight collaboration” in the areas of DTCO and PPA maximisation for an on-time silicon delivery that hit performance and efficiency demands.

TSMC’s head of the ecosystem and alliance management division, Dan Kochpatcharin echoed this, calling the AI-optimised CSS “a prime example” of Arm-TSMC collaboration helping designers push semiconductor innovation’s boundaries for unmatched AI performance and efficiency.

“Together with Arm and our Open Innovation Platform® (OIP) ecosystem partners, we empower our customers to accelerate their AI innovation using the most advanced process technologies and design solutions,” Kochpatcharin emphasised.

Arm isn’t trying to compete with customers, but rather enable faster time-to-market by providing optimised designs for neural processors delivering cutting-edge AI performance.

As Arm’s Chris Bergey said, “We’re combining a platform where these accelerators can be very tightly coupled” to customer NPUs.

Essentially, Arm provides more refined, “baked” designs customers can integrate with their own accelerators to rapidly develop powerful AI-driven chips and devices.

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The Blackwell platform promises up to 25 times lower cost and energy consumption compared to its predecessor: the Hopper architecture. Named after pioneering mathematician and statistician David Harold Blackwell, the new GPU architecture introduces six transformative technologies.

“Generative AI is the defining technology of our time. Blackwell is the engine to power this new industrial revolution,” said Jensen Huang, Founder and CEO of NVIDIA. “Working with the most dynamic companies in the world, we will realise the promise of AI for every industry.”

The key innovations in Blackwell include the world’s most powerful chip with 208 billion transistors, a second-generation Transformer Engine to support double the compute and model sizes, fifth-generation NVLink interconnect for high-speed multi-GPU communication, and advanced engines for reliability, security, and data decompression.

Central to Blackwell is the NVIDIA GB200 Grace Blackwell Superchip, which combines two B200 Tensor Core GPUs with a Grace CPU over an ultra-fast 900GB/s NVLink interconnect. Multiple GB200 Superchips can be combined into systems like the liquid-cooled GB200 NVL72 platform with up to 72 Blackwell GPUs and 36 Grace CPUs, offering 1.4 exaflops of AI performance.

NVIDIA has already secured support from major cloud providers like Amazon Web Services, Google Cloud, Microsoft Azure, and Oracle Cloud Infrastructure to offer Blackwell-powered instances. Other partners planning Blackwell products include Dell Technologies, Meta, Microsoft, OpenAI, Oracle, Tesla, and many others across hardware, software, and sovereign clouds.

Sundar Pichai, CEO of Alphabet and Google, said: “We are fortunate to have a longstanding partnership with NVIDIA, and look forward to bringing the breakthrough capabilities of the Blackwell GPU to our Cloud customers and teams across Google to accelerate future discoveries.”

The Blackwell architecture and supporting software stack will enable new breakthroughs across industries from engineering and chip design to scientific computing and generative AI.

Mark Zuckerberg, Founder and CEO of Meta, commented: “AI already powers everything from our large language models to our content recommendations, ads, and safety systems, and it’s only going to get more important in the future.

“We’re looking forward to using NVIDIA’s Blackwell to help train our open-source Llama models and build the next generation of Meta AI and consumer products.”

With its massive performance gains and efficiency, Blackwell could be the engine to finally make real-time trillion-parameter AI a reality for enterprises.

See also: Elon Musk’s xAI open-sources Grok

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The report states that MGX — a state-backed group in Abu Dhabi — is in discussions to support OpenAI’s venture to build AI chips in-house. This information comes from two individuals with knowledge of the discussions.

In order to achieve its goal of creating semiconductor chips internally, OpenAI is reportedly seeking investments worth trillions of dollars from investors worldwide. By manufacturing its own chips, the San Francisco-based company aims to reduce its reliance on Nvidia, the current global leader in semiconductor chip technology.

As part of its funding efforts, OpenAI struck a deal with Thrive Capital in February 2023, which reportedly increased the company’s valuation to more than $80 billion, marking an almost threefold increase in under 10 months.

This comes as the UK semiconductor sector gains enhanced access to research funding through the country’s participation in the EU’s ‘Chips Joint Undertaking’.

The UK’s participation in the Chips Joint Undertaking provides the British semiconductor sector with enhanced access to a €1.3 billion pot of funds set aside from Horizon Europe to support research in semiconductor technologies up to 2027. The move is backed by an initial £5 million from the UK government this year, with an additional £30 million due to support UK participation in further research between 2025 and 2027.

“Our membership of the Chips Joint Undertaking will boost Britain’s strengths in semiconductor science and research to secure our position in the global chip supply chain,” said Technology Minister Saqib Bhatti. “This underscores our unwavering commitment to pushing the boundaries of technology and cements our important role in shaping the future of semiconductor technologies around the world.”

Back in the UAE, MGX — the group behind the potential investment in OpenAI — is an AI-focused fund launched earlier this week and headed by the UAE’s national security adviser, Sheikh Tahnoon Bin Zayed al-Nahyan. The fund was established in collaboration with G42 and Mubadala, with G42 having already entered into a partnership with OpenAI in October 2023 as part of the company’s Middle East expansion.

During the G42 partnership deal, OpenAI CEO Sam Altman stated that they plan to bring AI solutions to the Middle East that “resonate with the nuances of the region.”

One of the sources briefed on the MGX fund emphasised, “They’re looking at creating a structure that will put Abu Dhabi at the centre of this AI strategy with global partners around the world.”

As the race to develop cutting-edge semiconductor technologies intensifies, both the UAE and the UK-EU are positioning themselves as key players.

(Photo by Wael Hneini on Unsplash)

See also: EU approves controversial AI Act to mixed reactions

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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)

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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)

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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)

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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)

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“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)

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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.

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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)

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