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

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 Edge Computing Expo.

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Coral was launched in 2019 with the goal of making edge AI more accessible. Google says that it’s witnessed strong demand since its launch – across industries and geographies – and needs a reliable partner able to help it scale.

ASUS IoT is a sub-brand of the wider ASUS brand that has decades of experience in global electronics manufacturing.

The sub-brand was the first partner to launch a Coral SoM (System-on-Module) product with the Tinker Edge T development board. Since then, ASUS IoT has integrated Coral accelerators into their intelligent edge computers and was first to release a multi Edge TPU device with the AI Accelerator PCIe Card.

In a blog post, Google wrote:

“We continue to be impressed by the innovative ways in which our customers use Coral to explore new AI-driven solutions.

And now with ASUS IoT bringing expanded sales, support, and resources for long-term availability, our Coral team will continue to focus on building the next generation of privacy-preserving features and tools for neural computing at the edge.”

Google will remain in control of the Coral brand and product portfolio but ASUS IoT will become the primary channel for sales, distribution, and support.

ASUS IoT will work to make Coral available in more countries while Google focuses its efforts on “building the next generation of privacy-preserving features and tools for neural computing at the edge.”

(Image Credit: Google)

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.

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The world’s largest tech exhibition, CES is taking place at the Las Vegas Convention Centre (LVCC) from 5-7 January this year.

MicroAI has partnered with communications solutions provider iBASIS to showcase Launchpad’s management capabilities at booth 12318.

Using connectivity provided by iBASIS, the demo will show how Launchpad manages MicroAI software running on embedded devices and handles data from multiple sensors.

It will also highlight Launchpad’s ability to securely administer a fleet of SIM cards within the same portal, thus simplifying mobile device management for customers.

MicroAI CEO, Yasser Khan, said: “Edge-native AI enables embedded AI software to run on microcontrollers and microprocessors in endpoint devices, transforming how AI can be made available right where data is captured.

Launchpad provides a straightforward way for companies to manage this – opening up new opportunities across many industry sectors.”

The company’s new security software will also be on show at its booth. MicroAI Security uses a proprietary embedded AI algorithm to detect, alert, and visualise cyber security attacks in real-time, running directly on edge and endpoint connected devices.

Use cases range from standard cyber attack mitigation to protecting critical assets, IoT devices, and industrial systems.

MicroAI will be demonstrating how its software can be used by manufacturers at the Trump International Tower a mile west of the LVCC.

By collaborating with KDDI, who are providing an LTE network for the system, MicroAI will show how its software enables data from sensors in a factory to be analysed by edge AI algorithms.

MicroAI Grid then enables a manufacturer to link this with multiple sites around the world, automatically sharing data and intelligence.

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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As we wrap up the year, it’s time to look ahead at what to expect from the AI industry in 2022.

Tackling bias

Our ‘Ethics & Society’ category got more use than most others this year, and with good reason. AI cannot thrive when it’s not trusted.

Biases are present in algorithms that are already causing harm. They’ve been the subject of many headlines, including a number of ours, and must be addressed for the public to have confidence in wider adoption.

Explainable AI (XAI) is a partial solution to the problem. XAI is artificial intelligence in which the results of the solution can be understood by humans.

Robert Penman, Associate Analyst at GlobalData, comments:

“2022 will see the further rollout of XAI, enabling companies to identify potential discrimination in their systems’ algorithms. It is essential that companies correct their models to mitigate bias in data. Organisations that drag their feet will face increasing scrutiny as AI continues to permeate our society, and people demand greater transparency. For example, in the Netherlands, the government’s use of AI to identify welfare fraud was found to violate European human rights.

Reducing human bias present in training datasets is a huge challenge in XAI implementation. Even tech giant Amazon had to scrap its in-development hiring tool because it was claimed to be biased against women.

Further, companies will be desperate to improve their XAI capabilities—the potential to avoid a PR disaster is reason enough.”

To that end, expect a large number of acquisitions of startups specialising in synthetic data training in 2022.

Smoother integration

Many companies don’t know how to get started on their AI journeys. Around 30 percent of enterprises plan to incorporate AI into their company within the next few years, but 91 percent foresee significant barriers and roadblocks.

If the confusion and anxiety that surrounds AI can be tackled, it will lead to much greater adoption.

Dr Max Versace, PhD, CEO and Co-Founder of Neurala, explains:

“Similar to what happened with the introduction of WordPress for websites in early 2000, platforms that resemble a ‘WordPress for AI’ will simplify building and maintaining AI models. 

In manufacturing for example, AI platforms will provide integration hooks, hardware flexibility, ease of use by non-experts, the ability to work with little data, and, crucially, a low-cost entry point to make this technology viable for a broad set of customers.”

AutoML platforms will thrive in 2022 and beyond.

From the cloud to the edge

The migration of AI from the cloud to the edge will accelerate in 2022.

Edge processing has a plethora of benefits over relying on cloud servers including speed, reliability, privacy, and lower costs.

Versace commented:

“Increasingly, companies are realising that the way to build a truly efficient AI algorithm is to train it on their own unique data, which might vary substantially over time. To do that effectively, the intelligence needs to directly interface with the sensors producing the data. 

From there, AI should run at a compute edge, and interface with cloud infrastructure only occasionally for backups and/or increased functionality. No critical process – for example,  in a manufacturing plant – should exclusively rely on cloud AI, exposing the manufacturing floor to connectivity/latency issues that could disrupt production.”

Expect more companies to realise the benefits of migrating from cloud to edge AI in 2022.

Doing more with less

Among the early concerns about the AI industry is that it would be dominated by “big tech” due to the gargantuan amount of data they’ve collected.

However, innovative methods are now allowing algorithms to be trained with less information. Training using smaller but more unique datasets for each deployment could prove to be more effective.

We predict more startups will prove the world doesn’t have to rely on big tech in 2022.

Human-powered AI

While XAI systems will provide results which can be understood by humans, the decisions made by AIs will be more useful because they’ll be human-powered.

Varun Ganapathi, PhD, Co-Founder and CTO at AKASA, said:

“For AI to truly be useful and effective, a human has to be present to help push the work to the finish line. Without guidance, AI can’t be expected to succeed and achieve optimal productivity. This is a trend that will only continue to increase.

Ultimately, people will have machines report to them. In this world, humans will be the managers of staff – both other humans and AIs – that will need to be taught and trained to be able to do the tasks they’re needed to do.

Just like people, AI needs to constantly be learning to improve performance.”

Greater human input also helps to build wider trust in AI. Involving humans helps to counter narratives about AI replacing jobs and concerns that decisions about people’s lives could be made without human qualities such as empathy and compassion.

Expect human input to lead to more useful AI decisions in 2022.

Avoiding captivity

The telecoms industry is currently pursuing an innovation called Open RAN which aims to help operators avoid being locked to specific vendors and help smaller competitors disrupt the relative monopoly held by a small number companies.

Enterprises are looking to avoid being held in captivity by any AI vendor.

Doug Gilbert, CIO and Chief Digital Officer at Sutherland, explains:

“Early adopters of rudimentary enterprise AI embedded in ERP / CRM platforms are starting to feel trapped. In 2022, we’ll see organisations take steps to avoid AI lock-in. And for good reason. AI is extraordinarily complex.

When embedded in, say, an ERP system, control, transparency, and innovation is handed over to the vendor not the enterprise. AI shouldn’t be treated as a product or feature: it’s a set of capabilities. AI is also evolving rapidly, with new AI capabilities and continuously improved methods of training algorithms.

To get the most powerful results from AI, more enterprises will move toward a model of combining different AI capabilities to solve unique problems or achieve an outcome. That means they’ll be looking to spin up more advanced and customizable options and either deprioritising AI features in their enterprise platforms or winding down those expensive but basic AI features altogether.”

In 2022 and beyond, we predict enterprises will favour AI solutions that avoid lock-in.

Chatbots get smart

Hands up if you’ve ever screamed (internally or externally) that you just want to speak to a human when dealing with a chatbot—I certainly have, more often than I’d care to admit.

“Today’s chatbots have proven beneficial but have very limited capabilities. Natural language processing will start to be overtaken by neural voice software that provides near real time natural language understanding (NLU),” commented Gilbert.

“With the ability to achieve comprehensive understanding of more complex sentence structures, even emotional states, break down conversations into meaningful content, quickly perform keyword detection and named entity recognition, NLU will dramatically improve the accuracy and the experience of conversational AI.”

In theory, this will have two results:

• Augmenting human assistance in real-time, such as suggesting responses based on behaviour or based on skill level.
• Change how a customer or client perceives they’re being treated with NLU delivering a more natural and positive experience.  

In 2022, chatbots will get much closer to offering a human-like experience.

It’s not about size, it’s about the quality

A robust AI system requires two things: a functioning model and underlying data to train that model. Collecting huge amounts of data is a waste of time if it’s not of high quality and labeled correctly.

Gabriel Straub, Chief Data Scientist at Ocado Technology, said:

“Andrew Ng has been speaking about data-centric AI, about how improving the quality of your data can often lead to better outcomes than improving your algorithms (at least for the same amount of effort.)

So, how do you do this in practice? How do you make sure that you manage the quality of data at least as carefully as the quantity of data you collect?

There are two things that will make a big difference: 1) making sure that data consumers are always at the heart of your data thinking and 2) ensuring that data governance is a function that enables you to unlock the value in your data, safely, rather than one that focuses on locking down data.”

Expect the AI industry to make the quality of data a priority in 2022.

(Photo by Michael Dziedzic 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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Arm plays a critical role in the global technology supply chain with its designs used for edge AI chips and processors for smartphones, tablets, desktops, and servers.

It’s of little surprise that Nvidia wants to bring Arm under its wing and is willing to pay $40 billion (£29 billion) for it.

Global regulators, including in the UK and EU, have launched investigations into the deal due to the widespread implications.

Holly Vedova, Director of the Bureau of Competition at the FTC, said in a statement:

“The FTC is suing to block the largest semiconductor chip merger in history to prevent a chip conglomerate from stifling the innovation pipeline for next-generation technologies.

Tomorrow’s technologies depend on preserving today’s competitive, cutting-edge chip markets. This proposed deal would distort Arm’s incentives in chip markets and allow the combined firm to unfairly undermine Nvidia’s rivals.

The FTC’s lawsuit should send a strong signal that we will act aggressively to protect our critical infrastructure markets from illegal vertical mergers that have far-reaching and damaging effects on future innovations.”

The complaint highlights that Nvidia already uses Arm’s designs for areas including DPU SmartNICs, CPUs for cloud computing, and advanced driving systems. The FTC is concerned that Nvidia would have an incentive to use its acquisition of Arm to limit competitors’ access to new designs.

Some of Nvidia’s rivals have offered to invest in Arm if it helps the company to remain independent.

Dr Lil Read, Analyst at GlobalData, commented:

“The Nvidia-ARM deal is on its last legs. The regulatory environment is much tougher now since Qualcomm has formed a consortium to invest in ARM.

The FTC won’t let it be – nor will the UK CMA or the EU regulator. It’s likely that even if the deal managed to clear those hurdles, Chinese regulators would throw another spanner in the works.

Tying the acquisition up for another two years is not in anyone’s interest – not Nvidia’s, and certainly not ARM’s. There could be hope for ARM if a non-chip firm recognises this opportunity for vertical integration – a trend that we increasingly see with the likes of Tesla and Apple.”

Arm founder Hermann Hauser even suggested the merger would amount to “surrendering the UK’s most powerful trade weapon to the US”.

Last month, UK Digital Secretary Nadine Dorries ordered the CMA (Competition & Markets Authority) to launch a “Phase Two” probe into the proposed merger.

As part of its ‘Phase One’ report, the CMA determined the merger has the possibility of a “substantial lessening of competition across four key markets”. Those markets are data centres, the Internet of Things, automotive, and gaming.

The CMA now has 24 weeks to conduct Phase Two of its investigation.

Nvidia, for its part, has promised to work with UK regulators to alleviate concerns. The company has already pledged to keep Arm in the UK and hire more staff.

“Arm is an incredible company and it employs some of the greatest engineering minds in the world,” said Jensen Huang, CEO of Nvidia. “But we believe we can make Arm even more incredible and take it to even higher levels.”

Today’s decision by the FTC to launch a lawsuit makes the likelihood of the merger proceeding ever more remote.

(Photo by NordWood Themes on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo North America on 11-12 May 2022.

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“From cloud to edge, Paravision’s goal is to help our partners develop and deploy transformative solutions around face recognition and computer vision,” said Joey Pritikin, Chief Product Officer at Paravision.

“With these sweeping updates to our product family, and with what has become possible in terms of accuracy, speed, usability and portability, we see a remarkable opportunity to unite disparate applications with a coherent sense of identity that bridges physical spaces and cyberspace.”

A new Scaled Vector Search (SVS) capability acts as a search engine to provide accurate, rapid, and stable face matching on large databases that may contain tens of millions of identities. Paravision claims the SVS engine supports hundreds of transactions per second with extremely low latencies.

Another scaling solution called Streaming Container 5 enables the processing of video at over 250 frames per second from any number of streams. The solution features advanced face tracking to ensure that identities remain accurate even in busy environments.

With more enterprises than ever looking to the latency-busting and privacy-enhancing benefits of edge computing, Paravision has partnered with Teknique to co-create a series of hardware and software reference designs that enable the rapid development of face recognition and computer vision capabilities at the edge.

Teknique is a leader in the development of hardware based on designs from California-based fabless semiconductor company Ambarella.

Paravision’s Face SDK has been enhanced for smart cameras powered by Ambarella CVflow chipsets. The update enables facial recognition on CVflow-powered cameras to achieve up to 40 frames per second full pipeline performance.

A new Liveness and Anti-spoofing SDK also adds new safeguards for Ambarella-powered facial recognition solutions. The toolkit uses Ambarella’s visible light, near-infrared, and depth-sensing capabilities to determine whether the camera is seeing a live subject or whether it’s being tricked by recorded footage or a dummy image.

On the mobile side, Paravision has released its Face SDK for Android. The SDK includes face detection, landmarks, quality assessment, template creation, and 1-to-1 or 1-to-many matching. Reference applications are included which include UI/UX recommendations and tools.

Last but certainly not least, Paravision has announced the availability of its first person-level computer vision SDK. The new SDK is designed to go “beyond face recognition” to detect the presence and position of individuals and unlock new use cases.

Provided examples of real-world applications for the computer vision SDK include occupancy analysis, the ability to spot tailgating, as well as custom intention or subject attributes.

“With Person Detection, users could determine whether employees are allowed access to a specific area, are wearing a mask or hard hat, or appear to be in distress,” the company explains. “It can also enable useful business insights such as metrics about queue times, customer throughput or to detect traveller bottlenecks.”

With these exhaustive updates, Paravision is securing its place as one of the most exciting companies in the AI space.

Paravision is ranked the US leader across several of NIST’s Face Recognition Vendor Test evaluations including 1:1 verification, 1:N identification, performance for paperless travel, and performance with face masks.

(Photo by Daniil Kuželev on Unsplash)

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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The ship is named after the Mayflower vessel which transported pilgrim settlers from Plymouth, England to Plymouth, Massachusetts in 1620. On its 400th anniversary, it was decided that a Mayflower for the 21st century should be built.

Mayflower 2.0 is a truly modern vessel packed with the latest technological advancements. Onboard edge AI computing enables the ship to carry out scientific research while navigating the harsh environment of the ocean—often without any connectivity.

“It will be entirely responsible for its own navigation decisions as it progresses so it has very sophisticated software on it—AIs that we use to recognise the various obstacles and objects in the water, whether that’s other ships, boats, debris, land obstacles, or even marine life,” Robert High, VP and CTO of Edge Computing at IBM, recently told Edge Computing News in an interview.

The Weather Company, which IBM acquired back in 2016, has been advising on the departure window for Mayflower’s voyage. Earlier this week, the Mayflower was given the green light to set sail.

Mayflower’s AI captain is developed by MarineAI and uses IBM’s artificial intelligence powers. A little fun fact is that the AI had to be trained specifically to ignore seagulls as they could appear to be large objects and lead to Mayflower taking unnecessary action to maneuver around them.

The progress of Mayflower can be viewed using a dashboard built by IBM’s digital agency iX.

A livestream from Mayflower’s onboard cameras is also available, but it can understandably be a little temperamental. IBM partnered with Videosoft, a company that specialises in live-streaming in challenging environments, to enable streaming over speeds of just 6kbps. However, there are times when Mayflower will be fully-disconnected—which even the best algorithms can’t overcome.

If the livestream is currently available, you can view it here.

Unlike its predecessor, Mayflower 2.0 won’t be reliant solely on wind power and will employ a wind/solar hybrid propulsion system with a backup diesel generator. The new ship also trades in a compass and nautical charts for navigation in favour of a state-of-the-art GNSS positioning system with SATCOM, RADAR, and LIDAR.

A range of sensors are onboard for scientific research including acoustic, nutrient, temperature, and water and air samplers. Edge devices will store and analyse data locally until connectivity is available. When a link has been established, the data will be uploaded to edge nodes onshore.

Mayflower is a fascinating project and we look forward to following its voyage. AI News will keep you updated on any relevant developments.

(Image Credit: IBM)

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

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If you recognise Xnor.ai, it’s likely because the company’s technology once powered the person-detection feature on Wyze’s popular cameras. Xnor.ai abruptly cancelled their contract with Wyze back in November – and now we know why.

In layman’s terms, edge computing means the processing is done on-device. On-device computation has significant advantages when it comes to reducing latency, improving performance, and retaining privacy.

https://www.youtube.com/watch?v=FG31XxX7ra8

Apple makes a big deal out of its privacy credentials. When people are becoming increasingly concerned about the data collection practices of competitors like Google, Amazon, and Microsoft, it’s a strategy that works.

Compared to all three of those cloud-focused competitors, many believe Apple has fallen behind when it comes to AI. With its Xnor.ai acquisition, Apple can boost its AI capabilities while saying that it’s protecting customers’ privacy by not sending it all up to the cloud like its rivals often do.

Xnor.ai started its journey as part of the nonprofit Allen Institute for AI. In 2017, the company spun off as its own entity headed by CEO Ali Farhadi.

While small change compared to what Apple acquired it for, Xnor.ai had already raised significant capital. In early 2017, it raised $2.7 million. In 2018, it raised $12 million. Both rounds were led by Madrona Venture Group.

Apple has been on something of an AI acquisition spree in recent years. In fact, a report by CBInsights last year found that Apple acquired more AI firms (20) than any other leading tech company in 2019. Google took second place with 14 acquisitions, followed by Microsoft with 10.

With the IoT becoming the next big thing, companies like Apple are looking to AI for ways to differentiate their products from competitors. Apple’s $200 million for Xnor.ai may seem a lot on paper, but it will almost certainly pay off in the long run.

Want to learn more about topics like this from thought leaders in the space? Find out more about the Edge Computing Expo, a brand new, innovative event and conference exploring the edge computing ecosystem.

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