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.

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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)

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

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)

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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.

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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.

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