This breakthrough represents a significant step in China’s ongoing efforts to become self-reliant in AI technology, especially in light of escalating US limitations on access to advanced semiconductors for its competitors.

According to the company’s Institute of AI, one of the models, TeleChat2-115B and another unnamed model were trained on tens of thousands of Chinese-made chips. This achievement is especially noteworthy given the tighter US export rules that have limited China’s ability to purchase high-end processors from Nvidia and other foreign companies. In a statement shared on WeChat, the AI institute claimed that this accomplishment demonstrated China’s capability to independently train LLMs and signals a new era of innovation and self-reliance in AI technology.

The scale of these models is remarkable. China Telecom stated that the unnamed LLM has one trillion parameters. In AI terminology, parameters are the variables that help the model in learning during training. The more parameters there are, the more complicated and powerful the AI becomes.

Chinese companies are striving to keep pace with global leaders in AI based outside the country. Washington’s export restrictions on Nvidia’s latest AI chips such as the A100 and H100, have compelled China to seek alternatives. As a result, Chinese companies have developed their own processors to reduce reliance on Western technologies. For instance, the TeleChat2-115B model has approximately 100 billion parameters, and therefore can perform as well as mainstream platforms.

China Telecom did not specify which company supplied the domestically-designed chips used to train its models. However, as previously discussed on these pages, Huawei’s Ascend chips play a key part in the country’s AI plans.

Huawei, which has faced US penalties in recent years, is also increasing its efforts in the artificial intelligence field. The company has recently started testing its latest AI processor, the Ascend 910C, with potential clients waiting in the domestic market. Large Chinese server companies, as well as internet giants that have previously used Nvidia chips, are apparently testing the new chip’s performance. Huawei’s Ascend processors, as one of the few viable alternatives to Nvidia hardware, are viewed as a key component of China’s strategy that will lessen its reliance on foreign technology.

In addition to Huawei, China Telecom is collaborating with other domestic chipmakers such as Cambricon, a Chinese start-up specialising in AI processors. The partnerships reflect a broader tendency in China’s tech industry to build a homegrown ecosystem of AI solutions, further shielding the country from the effects of US export controls.

By developing its own AI chips and technology, China is gradually reducing its dependence on foreign-made hardware, especially Nvidia’s highly sought-after and therefore expensive GPUs. While US sanctions make it difficult for Chinese companies to obtain the latest Nvidia hardware, a black market for foreign chips has emerged. Rather than risk operating in the grey market, many Chinese companies prefer to purchase lower-powered alternatives such as previous-gen models to maintain access to Nvidia’s official support and services.

China’s achievement reflects a broader shift in its approach to AI and semiconductor technology, emphasising self-sufficiency and resilience in an increasingly competitive global economy and in face of American protectionist trade policies.

(Photo by Mark Kuiper)

See also: Has Huawei outsmarted Apple in the AI race?

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During the regular trading session on Tuesday, NVIDIA’s share price experienced a near-10% drop. The fall wiped £212 billion from its market value, marking the largest single-day loss for a US company in history.

While the wider market experienced a sell-off fueled by concerns over weak US manufacturing data, NVIDIA was hit particularly hard after Bloomberg reported that the US Department of Justice issued subpoenas to NVIDIA and other tech firms. 

Officials are reportedly concerned that NVIDIA’s business practices may be hindering client flexibility in switching to alternative semiconductor suppliers. Additionally, there are concerns about potential penalties imposed on buyers who opt not to exclusively utilise NVIDIA’s AI chips. Such actions would represent an escalation of the ongoing US antitrust investigation, bringing the government a step closer to formally charging NVIDIA.

In response, NVIDIA asserted its belief that its success is based “on merit, as reflected in our benchmark results and value to customers, who can choose whatever solution is best for them.” 

This latest downturn adds to the recent volatility experienced by NVIDIA and other AI-related stocks, such as Google, Apple, and Amazon. Investors are grappling with uncertainty surrounding the timeline for tangible benefits and concrete returns from the much-touted AI revolution.

Analysts suggest that investors are seeking greater clarity on the trajectory of gross margins as production of NVIDIA’s new Blackwell chip increases. Furthermore, they are eager for more concrete evidence that AI is delivering tangible returns for customers.

After a 9.5% decline on Tuesday alone and a 14% drop since last week’s earnings report, NVIDIA’s stock has shown marginal signs of recovery in today’s trading session, registering a modest 0.64% increase at the time of writing.

Looking ahead, NVIDIA will need to convince investors of its growth potential not only for 2025 but also for 2026. While Wall Street currently focuses on Blackwell chip shipments, there is increasing interest in the company’s next-generation chip offering.

(Photo by Sebastian Molina)

See also: xAI breaks records with ‘Colossus’ AI training system

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This acquisition, reportedly valued at about $600 million, is not SoftBank’s first foray into the UK tech scene.

In 2016, SoftBank controversially acquired British chip designer Arm in a much larger deal. However, the Graphcore purchase comes at a lower valuation than the total funding the company is said to have raised, which was around $700 million.

Graphcore will continue to operate under its own name and maintain its headquarters in Bristol, UK. The company also retains its offices in Cambridge, London, Gdansk, and Hsinchu, signalling SoftBank’s commitment to preserving Graphcore’s established presence and operations.

Nigel Toon, co-founder and CEO of Graphcore, said: “This is a tremendous endorsement of our team and their ability to build truly transformative AI technologies at scale, as well as a great outcome for our company.”

Toon went on to emphasise the ongoing demand for AI compute and the work that remains to be done in improving efficiency, resilience, and computational power to fully realise AI’s potential.

Graphcore’s key offering is a range of “Intelligence Processing Units” – accelerators designed specifically for AI workloads – along with a software stack that allows developers to utilise its hardware effectively.

The company’s technology has often impressed. In 2020, a Graphcore device outperformed an Nvidia A100 GPU, and in another instance, its hardware halved the time required to handle a GPU-based drug discovery workload.

Despite these technological successes, Graphcore has struggled to generate significant revenue and achieve profitability. In 2022, the company reported revenue of just $2.7 million – a 46 percent year-on-year decrease – while operating expenses reached $206.8 million.

Vikas J. Parekh, Managing Partner at SoftBank Investment Advisers, commented: “Society is embracing the opportunities offered by foundation models, generative AI applications, and new approaches to scientific discovery.

“Next generation semiconductors and compute systems are essential in the AGI journey, we’re pleased to collaborate with Graphcore in this mission.”

The mention of AGI (Artificial General Intelligence) in Parekh’s statement suggests that SoftBank sees Graphcore’s technology as a key component in the pursuit of more advanced AI systems that can match or exceed human-level intelligence across a wide range of tasks.

Graphcore has built a reputation as a leading employer in the UK’s high-tech economy, and the company has committed to continuing its investment in creating high-skilled jobs across various disciplines.

The acquisition of Graphcore by SoftBank is likely to provide the AI chipmaker with significant resources and opportunities for expansion. It also reflects the increasing competition in the AI chip market, where companies like NVIDIA, Intel, and AMD have been vying for dominance.

As AI continues to permeate various sectors of the economy and society, the demand for specialised AI hardware is expected to grow. Graphcore’s integration into SoftBank’s portfolio positions both companies to capitalise on this trend.

See also: PC market finds new momentum amid AI interest

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 other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The KL730 auto-grade NPU chip packs an integrated Image Signal Processor (ISP) and promises to bring secure and energy-efficient AI capabilities to an extensive range of applications, spanning from enterprise-edge servers to smart home appliances and advanced driving assistance systems.

The KL730 sets itself apart as a groundbreaking chip specifically designed to accommodate artificial intelligence, boasting Kneron’s renowned energy-efficient and secure technology innovation. Featuring a cutting-edge peripheral interface that seamlessly connects various digital signals like images, videos, audio, and millimetre waves, the chip unlocks the potential for diverse AI applications across multiple industries.

Notably, the KL730 addresses a key barrier to the widespread adoption of AI technology: the high costs associated with energy-inefficient hardware.

The KL730 achieves an impressive 3-4x leap in energy efficiency compared to its predecessors and claims to be up to 2x more energy efficient than major competitors in the industry.

Albert Liu, Founder and CEO of Kneron, said:

“Running AI requires AI-dedicated chips with an architecture that is completely different from anything we’ve seen before. A simple re-appropriation of adjacent technologies, such as graphics-dedicated GPU chips, simply isn’t going to do the job.

The KL730 is a game-changer for edge AI. With its unprecedented efficiency and support for transformer neural networks, we are empowering users across industries to unlock the full potential of AI without compromising on data privacy and security.”

Kneron has long championed edge AI without the need for cloud connectivity and has continually advanced secure capabilities through a series of lightweight yet scalable chips.

In 2021, Kneron introduced the KL530—a pioneering edge AI chip that supports transformer neural networks, forming the backbone of GPT (Generative Pre-trained Transformer) models.

The introduction of the KL730 to the lineup provides a base-level compute power ranging from 0.35-4 effective tera operations per second, broadening its capacity to support cutting-edge lightweight GPT large language models such as nanoGPT.

The KL730 stands out as a powerful catalyst for transforming security in the AIoT landscape, enabling users to run GPT models partially or fully offline.

By leveraging Kneo, Kneron’s proprietary and secure edge AI network, the KL730 allows AI to reside on users’ edge devices and affords them greater control over data privacy. The implications span across industries – from enterprise server solutions to vehicles to AI-powered medical devices.

Bolstered security fosters increased collaboration between devices while preserving privacy. For instance, engineers can design new semiconductor chips without exposing confidential data to major cloud companies running data centres.

Since its establishment in 2015, Kneron has consistently earned accolades for its reconfigurable NPU architecture and has garnered recognition, including the prestigious IEEE Cas Society’s Darlington Award for breakthrough technologies.

Kneron serves a diverse clientele spanning AIoT, security, automotive, and edge server applications. Renowned companies such as Toyota, Quanta, Hanwha, and Dessmann have entrusted Kneron’s expertise to fuel their technological advancements.

Companies eager to explore the possibilities enabled by the KL730 shouldn’t have to wait long, with Kneron saying that samples will be available “soon”.

(Image Credit: Kneron)

See also: IBM Research unveils breakthrough analog AI chip for efficient deep learning

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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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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.

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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.

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The tech giant is often considered as “China’s Google” and, just like its Western counterpart, is one of the largest AI companies in the world.

“AI technology is growing increasingly complex, and integrated innovation has made AI more powerful,” said Haifeng Wang, CTO of Baidu.

“As AI technology plays an expanding role in a wider range of industries and drives a new era of technological revolution and industrial transformation, it is increasingly important to lower the threshold for different real-world applications and to increase accessibility to AI development platforms.”

At Baidu World 2021, the company made two significant AI announcements.

The first is Baidu Brain 7.0 which promises deeper integration of knowledge sources and deep learning. The open platform now features language comprehension and reasoning.

Baidu’s latest AI platform version works in tandem with the company’s new Kunlun II chip. The chip uses a 7nm process to match the current leaders including Graphcore and Huawei.

Kunlun II is equipped with Baidu’s second-gen XPU architecture. Over the previous generation, Baidu claims the latest Kunlun chip offers 2-3x more processing power.

The latest chip from Baidu works with the company’s open-source deep learning framework PaddlePaddle that has been used by more than 3.6 million developers around the world to build 400,000 AI models.

Baidu claims the models built using PaddlePaddle have led to applications to help water management systems run more efficiently, improve quality control in manufacturing, and even help athletes improve their training.

(Image Credit: Baidu)

Find out more about Digital Transformation Week North America, taking place on 9-10 November 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

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“How could it be that Tesla, who has never designed a chip before – would design the best chip in the world?” Musk boasted in 2019. 

Select Car Leasing tested Tesla’s full self-driving microchip, which is developed by Samsung, against 12 other innovations:

Against some of mankind’s greatest innovations, Tesla’s chip is only second to something not designed by any of us—the human brain.

To put the power of Tesla’s chip in perspective, the research highlights just how many of each device below it that it could power. The mind-blowing standouts for me are that it could power 180 RAF F-35 Fighter Jets or 36 Mars Perseverance rovers.

All of this performance was achieved with just 15 watts of increased power consumption over Tesla’s previous microchip. Furthermore, it’s 20 percent cheaper.

“Elon Musk has sent a clear message that the future is in real-world AI. If businesses want to realise the benefits of deploying AI and the competitive advantages that come with it, then they have to be taking AI out of the lab and into the real world, so that AI can learn from the uncontrollable mess that living in the real world involves,” comments Daniel Warner, CEO and Founder of LGN.

(Image Credit: Tesla)

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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Bristol-based Graphcore ranked number one on Fast Company’s top 10 most innovative AI companies of 2020 list. Nvidia, for comparison, ranked fifth.

Fast Company’s confidence in Graphcore clearly isn’t misplaced. Announcing its GC200 processor, Graphcore says its new chip is the world’s most complex.

The GC200 processor boasts 59.4 billion transistors and takes the crown from Nvidia’s A100 as the world’s largest. The A100 was announced by Nvidia earlier this year and features 54 billion transistors.

Each GC200 chip has 1,472 independent processor cores and 8,832 separate parallel threads, all supported by 900MB of in-processor RAM.

Graphcore says that up to 64,000 of the 7nm GC200 chips can be linked to create a massive parallel processor with around 16 exaflops of computational power and petabytes of power. Such a system would be able to support AI models with trillions of parameters.

“We are impressed with Graphcore’s technology for energy-efficient construction and execution of large, next-generation ML models, and we expect significant performance gains for several of our AI-oriented research projects in medical imaging and cardiac simulations,” comments Are Magnus Bruaset, Research Director at Simula Research Laboratory.

“We are also pursuing other avenues of research that can push the envelope for Graphcore’s multi-IPU systems, such as how to efficiently conduct large-scale, sparse linear algebra operations commonly found in physics-based HPC workloads.”

The GC200 is just the second chip to be launched by Graphcore. Compared to the first generation, the GC200 delivers an up to 9.3x performance increase.

Graphcore’s founders believe the IPU approach that the company is taking is more efficient than Nvidia’s GPU route. The ability to scale up to thousands of IPU processors in existing compute infrastructures could mean that the cost could be 10-20x lower than using GPUs.

Back in February, Graphcore announced that it had raised $150 million in funding for its R&D. The company’s total valuation is $1.95 billion.

Graphcore was fortunate to have secured its cash before the COVID-19 pandemic really hit – with many startups reporting difficulties obtaining vital funding where there was previous interest. Undoubtedly, the GC200 will help to power research to get us through this pandemic and all the other challenges the world faces now and in the future.

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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Speaking during the company’s Innovation Summit in Santa Clara, Intel Executive VP Navin Shenoy revealed a new focus on AI development.

The company’s AI-focused Xeon processors generated $1 billion in revenues during 2017. By 2022, it expects to be generating around $10 billion per year.

AI is set to be implemented in many areas of our lives in the coming years, across a variety of devices.

Shenoy claims recent breakthroughs have increased the company’s AI performance by 200x since 2014. He teases further improvements are on their way in upcoming releases.

The company will be launching its ‘Cascade Lake’ Xeon processor later this year with 11 times better performance for AI image recognition.

Arriving in 2019 will be ‘Cooper Lake’ which uses 14-nanometer manufacturing and will feature even better performance. In 2020, however, the company is targeting ‘Ice Lake’ with 10-nanometer manufacturing technology.

“After 50 years, this is the biggest opportunity for the company,” says Shenoy. “We have 20 percent of this market today.”

The admission it currently has a small share of the market today is bold and shows the company is confident about significantly upping that percentage in the coming years. It faces significant competition from Nvidia in particular.

Intel’s revenues were around a third data-centric five years ago. Now, it’s around half of Intel’s business.

Shenoy’s comments today show how seriously Intel is taking its AI business and the firm’s confidence it will be a major player.

What are your thoughts on Intel’s AI business? Let us know in the comments.

 Interested in hearing industry leaders discuss subjects like this and sharing their use-cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London and Amsterdam to learn more. Co-located with the  IoT Tech Expo, Blockchain Expo and Cyber Security & Cloud Expo so you can explore the future of enterprise technology in one place.

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