Robotics and Automation News, August 19

ROBOTICS AND AUTOMATION NEWS

Issue 28 August 2019

Speed kings:

T he m ont hl y m ag az i ne for t he robot i c s and autom at i on i ndus t r y

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Robotics & Automation News

AI know what you mean

Editorial

Contents Top AI chip companies: A macro step change inferred from the micro scale

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Abdul Montaqim, Editor

t’s quite possible that if people are honest about their attitudes towards artificial intelligence and the effects AI is likely to have on all aspects of the economy and society in the future, then they will admit they are somewhat nervous at least.

There are plenty of reasons to be nervous about AI. First thing – and probably the most fundamental thing – is that most people might be fooled into believing that a computer will not have any prejudices.

Therefore, an AI-based system would be fairer, more just, in its assessment and judgment of cases and individuals in every circumstance.

This, however, is not necessarily the case, as you can imagine. The AI has to be programmed by human coders in the first place, and they can encode whatever prejudices and unjust systems they want into the AI and the AI would have no choice but to follow this programming.

Which, of course, means that AI isn’t as intelligent – and certainly not “conscious” – as some of its proponents would have us accept, and in the way most of us understand. Human judgment, exercised by real humans, is, therefore, still required.

The most zealous of proponents of AI do not seem to be very keen on publicity. But judging by how much of the economy and society is given over to the decision-making of AI, it can be deduced that these proponents are large in number and powerful in their scope of activity.

And worst of all, they are almost totally unaccountable.

Perhaps even more insidious is that the people who are apparently speaking out against AI and the technology’s growing and direct influence on every molecule within the minutiae of our lives are probably the “bait” with which the AI masters catch “subversive” people who may cause problems in the future, possibly by organising any effective opposition against an AI-dominated world, especially since AI is becoming merely another weapon in the hands of those whose only interest is to control everyone else’s lives to the nth degree.

It’s difficult to know what to do about this situation. Ordinary, everyday people may have thought they had developed what might be called a “solution for the ages” to deal with these matters, this “AI tyranny” as some call it. This “solution for the ages” has different names and components – such as representative politics, democracy, the Magna Carta, the Constitution, Bill of Rights, or some such social contract which theoretically prevents powerful people at the upper echelons of the socio-economic ladder from subjugating and exploiting others further down by continuously monitoring them, stealing their ideas and fruits of their labour.

This might be the most important battle in human history, certainly if you believe in the claim that AI will be the “last invention”, after which humans will no longer have to invent anything or even do anything. Meaning, AI will do absolutely everything for us, including have all our fun for us, no doubt. l editorial@roboticsandautomationnews.com

Truck loading Automatic Truck Loading Systems reduce loading and unloading times to just a few minutes VIEW technology: augmented reality, artificial intelligence, and smart glasses for the 2019 News: Mercedes and BMW launch automated driving pact

News: Hanwha Aerospace acquires US component manufacturer Logistics: The changing face of the supply chain

Recycling: Cleaning up in Japan’s demolition market

Robotics and Automation News

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News

News

Aurora shows off its automated palletizing robot

New Zealand automation company Aurora demonstrates and describes its robotic palletizing system Palletizing equipment is a critical component of many end-of-line processes. Without the right system in place, inefficiencies and production downtime can harm your bottom line.

One of Aurora’s popular solutions is the Fuji-Ace Robotic Palletizer. Often cited as the most advanced palletizer in the world, it is a high-capacity, energy efficient, easyto-use machine that is streamlining the processes of packing and manufacturing facilities across the world.

In the words of one satisfied customer, site engineer Kendell Chapman of Seales Window: “The output of the system has improved capabilities over our last system.

“The day-to-day operation of the system is very usable, the maintenance of the system is very low… [others] won’t be let down by the service and performance of the machine.”

The Fuji-Ace EC-102 Palletizer features power consumption of only 3.5 kW, the lowest energy consumption for a machine of its type in the world.

HP opens 3D centre of excellence in Spain HP has opened a new 3D printing and digital manufacturing centre of excellence in Barcelona, Spain – one of the world’s largest and most advanced research and development facilities for those technologies.

The new centre brings together hundreds of the world’s leading additive manufacturing experts in more than 150,000 square feet of space.

The 3+ acre facility at HP’s Barcelona campus is dedicated to the development of HP’s industrial 3D printing portfolio.

Automotive

Mercedes and BMW launch automated driving pact

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aimler-Mercedes-Benz and BMW have entered into a “co-operation” agreement on the development of autonomous cars, or “automated driving”, as the two companies call it.

Representatives from the two companies have signed an agreement for a long-term strategic cooperation, which will focus on joint development of next-generation technologies for driver assistance systems, automated driving on highways and automated parking.

The developments are intended to vehicles that are classed as “Level 4” – or “full autonomy with human” – on the SAE’s categorisation. SAE is a global engineering association which has published a “levels of driving automation” table which goes from Level 0 for no autonomy to Level 5 for full autonomy.

In addition, further talks are planned to extend the cooperation to higher levels of automation in urban areas and city centres.

This underscores the long-term and lasting nature of the undertaking, which will extend to encompass a scalable platform for automated driving, say the companies.

The non-exclusive cooperation is also open to other original equipment manufacturers and technology partners, with results being made available to other OEMs under license.

A key aim of the cooperation is the swift market launch of the technology, which is expected to feature in passenger car systems for private customers from 2024.

The two companies will each implement the technologies in their respective series products independently. The cooperation will see more than 1,200 specialists working together, often in mixed teams.

They will be based at locations including the Mercedes-Benz Technology Centre in Sindelfingen, the Daimler Testing and Technology Centre in Immendingen and the BMW Group Autonomous Driving Campus in Unterschleissheim, near Munich.

Efforts will focus on developing a scalable architecture for driver assistance systems, including sensors, as well as a joint data centre for data storage, administration and processing, and the development of functions and software.

Along with Aptiv, Audi, Baidu, Continental, Fiat Chrysler, HERE, Infineon, Intel and Volkswagen, the BMW Group and Daimler have published a white paper entitled Safety First for Automated Driving.

As well as covering all relevant safety methods for Level 3/4 SAE automated driving, the paper introduces a traceability system, which extends from the primary goal – being safer than the average driver – right down to the individual safety objectives of the various components.

uro, a developer of driverless roadgoing vehicles, is partnering with fast-food chain Domino’s to deliver pizzas to customers at their homes.

A statement on Nuro’s blog says: “We’re thrilled to announce that we’ve partnered with Domino’s, the world’s largest pizza company, to deliver pizza autonomously.”

Nuro’s self-driving delivery service first launched in 2018 through its partnership with

Kroger, one of the largest grocery chains in the US. “Partnering with Domino’s marks an important step on our journey to become the autonomous delivery partner of choice for retailers of all kinds,” says Nuro.

Domino’s is one of the leading pizza companies famous for delivery, selling 3 million pizzas per day from over 16,000 stores worldwide.

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Nuro and Domino’s in pizza partnership

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Aerospace

News

News

Hanwha Aerospace acquires US component manufacturer

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anwha Aerospace has signed a deal to fully acquire EDAC Technologies, an American aircraft engine component manufacturer. The deal is expected to be worth around USD 300 million, which will be finalised once negotiations are completed.

The Korean company made a preliminary bid for EDAC Technologies in April of 2019 and entered a period of due diligence before making a final offer.

EDAC Technologies is an aircraft engine component manufacturer based in Connecticut, with a workforce of 590 people.

It recorded USD 150 million in revenue in 2018.

Its primary customers are GE and Pratt & Whitney and it produces aircraft engine components such as integrally bladed rotors and engine cowling.

Through this acquisition, Hanwha Aerospace will be able to secure a foothold alongside leading global aircraft engine manufacturers, obtain more orders, and expand its product portfolio.

EDAC Technologies also provides high-end processing capabilities to make Hanwha Aerospace even more competitive.

The acquisition provides design, development, and technological capabilities for Hanwha Aerospace to become a Tier-1 supplier for risk and revenue sharing partnerships. In addition, it provides a base from which Hanwha Aerospace can expand its American operations.

“Based on the advanced technology we’ve acquired over the past 40 years and our product quality, we’ve recently entered into risk and revenue sharing partnerships, which have high barriers of entry in the global aircraft engine market,” said President and CEO of Hanwha Aerospace Hyun-woo Shin.

He added: “With our acquisition of EDAC, we will continue to expand our global presence to achieve our goal of becoming the world’s number one partner in the aircraft engine industry.”

Due to the increasing amount of air travel and cargo volume, the global aircraft engine component market is expected to grow by 6% annually and reach USD 54.2 billion by 2025. To help foster this growth, Hanwha announced that it will invest KRW four trillion by 2022 to expand its aircraft component and defense units overseas.

Hanwha Aerospace is Korea’s only gas turbine engine manufacturer. It entered the aircraft engine industry in 1979, providing maintenance services for gas turbine engines. Since then, Hanwha Aerospace has produced over 8,600 aircraft engines.

pple has introduced the all-new Mac Pro, a completely redesigned workstation and unveiled Apple Pro Display XDR. Designed for maximum performance, expansion and configurability, the Mac Pro features workstation-class Xeon processors up to 28 cores, a highperformance memory system with a massive 1.5TB capacity, eight PCIe expansion slots

and a graphics architecture featuring the world’s most powerful graphics card.

It also introduces Apple Afterburner, an accelerator card that enables playback of three streams of 8K ProRes RAW video simultaneously. Pro Display XDR features a 32inch Retina 6K display with P3 wide and 10-bit colour, 1,600 nits of peak brightness, 1,000,000:1 contrast ratio and a wide viewing angle.

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Apple unveils Mac Pro with new display

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IBM develops supercomputer for Total

IBM has built Pangea III, which it claims is the world's most powerful commercial supercomputer, for Total, a global energy company operating in more than 130 countries. The new IBM POWER9-based supercomputer will help Total more accurately locate new resources and better assess the potential associated revenue opportunities.

According to Total, Pangea III requires 1.5 Megawatts, compared to 4.5 MW for its predecessor system. Combined with the increased performance of Pangea III, Total has reported that they have observed that the new system uses less than 10% the energy consumption per petaflop as its predecessor.

Pangea III is being built using the same IBM POWER9 AI-optimised, highperformance architecture as used in the U.S. Department of Energy's Summit and Sierra supercomputers.

Because IBM POWER9 is optimised to take advantage of attached accelerators, it is designed to help Total not only improve performance but also improve energy efficiency in their HPC workloads.

"Pangea III's additional computing power enhances Total's operational excellence," stated Arnaud Breuillac, President of Total Exploration & Production.

"It enables Total to reduce geological risks in exploration and development, accelerate project maturation and delivery, and increases the value of our assets through optimised field operations, with all this at lower cost."

Exploring new oil and gas prospects, Total must first create an accurate picture of what lies underground, through the use of seismic data acquisition during exploration campaigns.

Geoscientists then use these images to identify where oil and gas resources lie. This process creates massive amounts of data that IBM POWER9 systems handle.

Features

VIEW technology

Augmenting reality

VIEW technology: Seth Patin, founder and CEO of LogistiVIEW, takes a look at augmented reality, artificial intelligence, and smart glasses for the 2019 holiday season.

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arehouse workers can be faster, safer, and more accurate this holiday season. LogistiVIEW’s “Visual Input Enabled Wearable” (VIEW) technology improves workforce productivity and job satisfaction by making processes hands free and eyes focused.

Intuitive voice and visual instructions make work easy and training rapid. These benefits can all be accomplished in time for the 2019 holiday season thanks to LogistiVIEW’s Connected Worker Platform.

Built on VIEW technology, it combines Computer Vision, Augmented Reality (AR), Artificial Intelligence (AI), and smart glasses so a warehouse management system (WMS) can see and use what it sees to collect data and instruct the workforce. editorial@roboticsandautomationnews.com

VIEW devices – smart glasses in supply chain WMS solutions are good at managing information, but not truly intuitive for the floor worker. This is primarily because lines of text are not the best way to tell someone how to do work; humans respond best to visual instructions.

For that reason, most companies discuss on-boarding and training in terms of days, or worse yet, weeks.

VIEW technology rethinks workforce instruction by creating a system that increases productivity and job satisfaction. It starts with rethinking workforce mobile technology with the VIEW device.

A VIEW device is a small-scale head-mounted computer with a high definition camera, field of sight display, audio input and output, positional awareness sensors, and wireless networking.

When properly integrated with a WMS, VIEW devices revolutionize warehouse operations by bringing a completely hands-free and AR directed worker experience. Today these types of devices are commonly called smart glasses. www.roboticsandautomationnews.com

VIEW technology

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VIEW technology rethinks workforce instruction by creating a system that increases productivity and job satisfaction.

It starts with rethinking workforce mobile technology with the VIEW device.

Seth Patin

Operational impact of VIEW devices VIEW technology works with a WMS to help it “know” what tasks a worker is supposed to perform as well as where a worker is located, what product the worker is holding, what product the worker is near, what physical obstacles surround the worker, and what other workers are in the vicinity.

Leveraging AR, VIEW technology directs workers via voice instructions and visual cues by overlaying information into the worker’s field of vision. The system knows where a worker needs to go and directs the worker via overlaid arrows and highlighting.

Once at the destination, workers execute workflows in the WMS by simply looking at the product as directed by the VIEW device or scanning the barcodes with a wearable Bluetooth scanner.

Inexperienced warehouse workers hired for the 2019 holiday season are able to pick a high-value serialized item by simply looking at the storage location barcode and scanning the product to be picked.

The system isolates all the scanned barcodes, catalogs meaning via AI filtering, and processes a pick transaction with the correct part number, serial number, and storage location without the user taking hands or eyes off the product.

Hands-free workflow improves efficiency while eyesfocused workflow and AI-validated data capture, reduce editorial@roboticsandautomationnews.com

Features the likelihood of critical errors.

Optimizing workflows and providing superior track and trace capabilities supports Lean Manufacturing, Six Sigma initiatives, and industry best-practices.

VIEW devices bring a new level of workforce accountability, which is of paramount importance for 2019 holiday picking, packing, and shipping. Operations management has an unprecedented level of information about workers’ activities.

Warehouse shift managers assist new, low-skilled labor by shadowing a worker remotely in real-time and reviewing the workers’ movements through the facility during the work shift.

Efficiency gains are quantifiable When a worker is confused about inventory at a location, rather than a supervisor walking across the entire warehouse, the supervisor simply shadows the user and talks to them through the questions using VIEW devices.

Warehouse managers can walk the floor and simply look at a QR code on the side of a row of pick bins to get a real-time report of productivity in that pick face for the shift.

Workforce management solutions are supercharged by the information obtained with VIEW devices driving quantifiable efficiency gains.

Just in time for the 2019 holiday season Rapidly implemented in as little as 60 days, these technologies can still generate optimal holiday profitability this year.

From 3PLs to warehouses and distribution centers, the urgent need for this workforce technology is undeniable.

The ability to simplify complex processes in a way that improves both productivity and job satisfaction means low-skilled warehouse labor can be faster, safer, and more accurate in fulfilling orders this holiday season. l About the author: Seth Patin is the Founder and CEO of LogistiVIEW, a software company that uses artificial intelligence and augmented reality on industrial smart glasses to improve the productivity and job satisfaction of task-oriented workers. Since starting his career at supply chain software vendor, RedPrairie (now JDA), over 15 years ago, Seth has worked in several roles as a technology vendor, customer, and consultant, eventually founding Accelogix, a consulting firm that delivers logistics technology solutions for mid-sized and large enterprise, in 2012.

Seth’s experience in logistics and his vision for a better approach to human/computer interaction led him to found LogistiVIEW in 2014 and lead the company to become a pioneer and leader in the development of connected workforce technology. www.roboticsandautomationnews.com

Features

Gearing up for truck loading automation

Truck Loading

Truck Loading: Automatic Truck Loading Systems reduce loading and unloading times to a few minutes, writes Jack Smylie, North America sales manager, Ancra Systems

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By adding elevators, lift platforms, turntables, and conveyors which interface with the automatic loading and unloading systems, the rate of truck turns increases dramatically.

Jack Smylie

s 3PLs (third-party logistics) companies, distribution centers, and warehouses all get ready for the all-important holiday picking, packing, and shipping season there is a fundamental flaw in how space utilization is conceived.

Most warehouse managers are sure if they had double the number of loading and unloading bays, their operation would be twice as efficient. Nothing could be further from the truth. The efficiency of truck unloading and loading occurs when that process is automated.

Fully automatic loading and unloading always require two combined systems: one system in the truck or trailer and a fixed installation on the loading platform or in the warehouse. Truck loading conveyors and other automated solutions allow increased efficiency.

By adding elevators, lift platforms, turntables, and conveyors which interface with the automatic loading and unloading systems, the rate of truck turns increases dramatically.

Automatic truck (un)loading systems, also known as ATLS moves product from standard systems to customized solutions.

Geared to industry sector applications and uniquely calculated to meet logistic requirements, streamlining trailer loading and unloading processes worldwide captures the core lean manufacturing principle of eliminating waste (time, resources, and labor).

The most commonly used truck loading and unloading systems are for shuttle services between production facilities and distribution centers.

Truck loading and unloading times are reduced from half an hour to a matter of minutes.

Return-on-investment times are short due to the immediate savings compared to the traditional method of using forklifts or hand pallets/trucks.

Labor savings are immediately realized with ATLS

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solutions since fewer logistics personnel are required thanks to the automation of the loading and unloading process. Fewer forklifts and associated training, maintenance, and repair costs drive another cost-savings.

Handling higher volumes Fewer loading and unloading docks are required because each dock can handle higher volumes. The time trucks are loaded and unloaded is significantly reduced; previously contemplated distribution center or warehouse expansion becomes unnecessary.

The high price of warehouse space and full capacity occupancy rates translate into a paradigm shift where the current number of docks is more than adequate once an ATLS system is implemented.

Cost-savings are realized as fewer trucks, trailers, and drivers are required due to increased fleet utilization. Less warehouse space is needed due to more concentrated flow of goods.

Less buffer stock is needed, allowing for JIT (Just-inTime) inventory, due to quicker inbound and outbound transportation of goods. Similarly, there is less space www.roboticsandautomationnews.com

Truck Loading

outside needed with the quicker turnaround times of trailers.

Reduced damage of goods and equipment due to controlled loading and unloading is a quantifiable benefit as well as creating a safer working environment for personnel, which generates employee satisfaction and retention.

The Skateloader system increases efficiency by 400 percent compared to traditional forklift truck loading There are different types of ATLS solutions. One of the most popular is the Skateloader system (pictured above).

Ultimate solution The Skateloader system was developed and engineered for the automated loading of non-modified trailers. When no trailer modifications are required, the Skateloader is the ultimate solution for one-shot loading of standard pallets for outbound transport.

The loading process takes approximately 6-8 minutes (depending on the required height adjustment and alignment).

The Skateloader has two special functionalities for editorial@roboticsandautomationnews.com

Features

loading. It is equipped with a scanning system controlled by dedicated software to ensure the correct alignment of the loading system with the trailer.

It is able to follow the trailer’s height which changes continuously due to the air suspension, thus ensuring a flawless loading process. The system deposits the pallets on the floor of the trailer in a controlled way, without any friction to the pallets and its cargo.

The Skateloader can be seamlessly integrated into any automated production or warehouse system. The Skateloader system increases efficiency by 400 percent, compared to traditional forklift truck loading. The shorter truck turnaround time at the dock is accomplished while creating a safer work environment, for employees and product. l About the author: Jack Smylie is the North America Sales Manager at Ancra Systems. Ancra Systems supplies material handling systems for automatic truck loading and unloading and the connection to new or existing internal transport conveyors.

Leading clients include DHL, Toyota, Bavaria, Federal Express, Procter & Gamble, and Friesland Foods.

Jack has many years of material handling and industrial packaging experience in multiple industries, both domestically and internationally. Jack studied Marketing at Kent State University and has been a guest speaker at multiple trade shows, university seminars, and radio programs. Jack can be reached at (908) 297-2731 or j.smylie@ancrasystems.com. www.roboticsandautomationnews.com

Features

Logistics and supply chain

The changing face of the supply chain Logistics and supply chain: An overview of ‘goods-toperson’ and ‘last-mile delivery’ technologies

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he term “goods to person” in the context of the logistics and supply chain sector refers to technology that brings items from locations within, for example, a warehouse to a human picker while he or she remains in one place, also within the warehouse.

And while “logistics” and “supply chain” are terms that are often used interchangeably, logistics usually refers to moving goods along public roads or out in the wider world, whereas supply chain refers to specific sections editorial@roboticsandautomationnews.com

within the journey that the goods make, such as their processing through a warehouse.

One of the technologies most readily associated with medium- to large-scale warehouses is the conveyor, which has been around for more than 100 years. But in the past few years, warehouse robots have been eroding the total dominance of conveyors by offering what are offering described as “flexible” supply chain systems, in contrast to the “fixed” nature of conveyors.

Both conveyors and warehouse robots can be described as “goods-to-person” systems: l a conveyor moves boxes full of various items through the warehouse, and human workers can be waiting at various points along the conveyor to pick out the www.roboticsandautomationnews.com

Logistics and supply chain

Features the overall supply chain market, an increasing number of warehouse managers are choosing robots over conveyors.

Additionally, there are many warehouses that do not use conveyors or robots. They might use an all-human workforce and system, with just traditional warehouse infrastructure. Or they might also use some vehicles such as forklift trucks and automated guided vehicles. AGVs are technically different from robots, which are often referred to as autonomous mobile robots, or AMRs.

specific items they need to fulfill an individual order from a customer; and l a warehouse robot can move boxes or entire shelf units through the warehouse to the human worker so he or she can remain stationary and simply pick out the items they need to fulfill a customer order.

The human picker – and the picking station – is central in both these scenarios. And although picking items might seem like it’s technically a simple job for a human, it’s not at all straightforward for a robot.

A number of companies are developing robotic solutions to try and replace the human picker, but at the moment, the human picker is the fastest and most reliable solution for this job.

It’s not often you can say that a human is faster and more reliable than a robot, but that’s the way it is right now in picking, so there’s not going to be much change there for a while.

But what is changing quite significantly is the technology surrounding the human picker or the picking station. The big transformation is the emergence of warehouse robots as a viable alternative to conveyors.

Although robots are still a relatively small percentage of editorial@roboticsandautomationnews.com

Conveying robotics and automation The difference between an AGV and an AMR is in the “a” – automated or autonomous. An AGV usually requires the placing of magnetic strips on the ground to guide it through the warehouse, or it can be led around by a human.

Meanwhile, an AMR requires only its internal mapping and artificial intelligence system to navigate, as well as recharge itself. And, increasingly, the AI within AMRs can mean they organise the warehouse in a way that fastmoving goods are located nearer to the picking station.

For companies such as Geek Plus Robotics, a supplier of warehouse robots, this hybrid technology landscape presents a once-in-a-generation opportunity.

The relatively new startup company is probably the leading supplier of warehouse robots in the world, having sold more than 7,000 units since its founding in 2014. Its success in this market has led it to develop ideas such as a driverless forklift truck, which it is already offering to the market.

Also, Robotics and Automation News has seen Geek Plus documents which show that the company is considering developing what’s called a “delivery robot” as part of its “smart logistics strategy”. More on that as we get it.

A spokesman for Geek Plus told Robotics and Automation News that the growth of e-commerce is placing additional demands on the supply chain and logistics sector, and some of the established giants do not have enough time or resources to provide the high level of customer services that it can to smaller and mediumsized companies.

Material handling, as the terms suggest, refers specifically to the movement of goods or items. This could be moving the material between a truck and a warehouse, on or off the truck, or in any similar situation.

You could perhaps think of it as the area where logistics connects with the supply chain.

There are numerous multi-billion-dollar material handling companies which specialise in supplying material handling services, but according to the Geek Plus spokesman, some of them find themselves having to sometimes turn away all but the largest customers because they simply don’t have the resources to allocate to anything else other than the larger, multimillion-dollar projects.

“The large integrators are concentrating on jobs that are valued at tens of millions of dollars, and are actually turning down small- and medium-sized projects that

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The large integrators are concentrating on jobs that are valued at tens of millions of dollars, and are actually turning down small- and mediumsized projects that might be worth half a million to a million.

Geek Plus

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Features

Universities are ideal grounds for delivery robots right now because they are enclosed spaces controlled by one management body, similar to an airport or large manufacturi ng facility or industrial park.

Logistics and supply chain might be worth half a million to a million,” he said.

A typical Geek Plus picking system consists of robots, robot charging stations, management software, and picking station, as well as shelf units that are compatible with the robot. Among the main advantages of a robotbased warehouse is that it can be installed faster and at a lower cost. The return on investment can, therefore, be achieved sooner.

But each warehouse operation has its own requirements, and conveyors are generally chosen by warehouses have the highest throughput of products.

Branching out It has been noted by experts within the field of warehouse robotics that the root technology the robotic vehicles use is essentially very similar to what you might find in autonomous vehicles outside of the warehouse. For example, driverless cars.

It’s a completely different level of sophistication and complexity, but basically, what we’re talking about are vehicles that can move autonomously and find their own way around a location.

Having said that, and to emphasise again, there is a world of difference between an autonomous car that is designed to drive along public roads and an AMR that operates exclusively within a warehouse.

But there is a relatively new machine that might be regarded as “bridge” in-between those two technologies, and that is the delivery robot.

The term “delivery robots” – within this sector at least – specifically refers to robots that deliver items using public footpaths and roads. They can, of course, be used in warehouses as well, or any other location, but they’re not really designed for that.

Robotics and Automation News recently published a list of the top 20 delivery robot companies, which shows that this is a technology that is growing and evolving very fast.

Who would have guessed that so many companies were already in this space?

Moreover, research suggests that the market for delivery robots will grow from about $12 billion last year to $34 billion by 2019.

The difficulties in categorising new technology – or

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our own lack of understanding – means that sometimes we overlook companies like Savioke, which produces a delivery robot of sorts. But that company has mainly been concentrating on supplying its robots to hotels, and the machine designed to carry very small items over short distances.

Perhaps the standard or typical delivery robot is produced by a company called Starship Technologies, which has developed a partnership with Mercedes, as illustrated in the picture above. The obvious implication is that robots are very likely to be integrated with traditional logistics vehicles.

Starship has also recently found significant commercial success in supplying its robots to university campuses, teaming up with food services company to supply what they describe as “the world’s largest fleet of delivery robots on a university campus” – which is about 25 robots in one place.

Universities are ideal grounds for delivery robots right now because they are enclosed spaces controlled by one management body, similar to an airport or large manufacturing facility or industrial park. This avoids all the difficulties of operating in public spaces, which are sometimes regulated in a way that is not welcoming to delivery robots sharing sidewalks with humans.

For example, San Francisco last year effectively banned delivery robots from its sidewalks until an acceptable regulatory framework was developed. But other states, such as Washington, are more accommodating, and tests are being carried out by a variety of companies – most notably, Amazon – in partnership with local authorities across the US. Similar tests within frameworks of public-private partnerships are also being carried out in Europe.

The Chinese way While the US and Europe develop what will probably end up being detailed regulations governing the operation of delivery robots in public spaces, delivery robot companies appear to have been given almost total freedom of the streets and roads by their government.

Maybe it’s because the Chinese government is newly enthusiastic about the latest technologies, such as artificial intelligence and all things robotic. Or – more likely – it’s basically a gap or loophole in the regulations surrounding vehicles that operate along sidewalks and roads.

How many people really understand this technology and its future effects on society anyway? Some might say, for example robots puts humans out of work. But even in China, logistics companies are finding it difficult to recruit humans to deliver parcels, and that labour shortage is very likely to get much worse over time.

That’s not to say there are no regulations concerning delivery robots at all. China? Of course not.

Speaking to Robotics and Automation News, a senior executive of Zhen Robotics explained that his company’s delivery robot does not enter residential building www.roboticsandautomationnews.com

Logistics and supply chain

complexes. Rather, it stops at the main gate and notifies the intended recipient. “The intended recipient then comes out to the main gate or entrance and picks up their items from the robot,” he said.

He added, however, that regulations in China relating to delivery robots is a “huge grey area”.

Interestingly, Zhen Robotics produces four types of robot, including one which is a warehouse robot, or AMR. (One of the robots can be seen in the picture below.) It’s probably the only company which currently produces a robot for warehouses and a robot for on-street delivery.

Another delivery robot company which has been growing fast in China is Neolix. We didn’t know much about this company until recently and have had to update the top 20 delivery robots list as a result.

We had originally thought that a robot pictured being used by Cainiao was developed by Cainiao itself – Cainiao being a massive logistics company that is majority-owned by e-commerce giant Alibaba.

But, in fact, that robot Cainiao is showing off was actually developed by Neolix.

A spokeswoman for Neolix confirmed this and provided a few more details about the startup. Not only is Neolix partnering with Cainiao, it is also working with Baidu, China’s equivalent of Google.

Moreover, Neolix is expanding into Europe, where it is preparing a test program that starts in September. While she would not provide details of the test, or which European company it is, she did say that Neolix has received many enquiries from numerous European countries about its product.

“In China, we have about 200 of our robots in operation,” she said. “Most of them are used as mobile vending machines. They are about 3 metres long and 1 metre wide, and can be stocked with a variety of items.

“One of these units is currently located in one of the largest public parks in Beijing.” editorial@roboticsandautomationnews.com

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In China, we have about 200 of our robots in operation.

Most of them are used as mobile vending machines.

They are about 3 metres long and 1 metre wide, and can be stocked with a variety of items.

Neolix

The Neolix vehicle is claimed to be the “world’s first level 4 unmanned vehicle”, level 4 referring the global engineering association SAE’s definition “high automation”, the highest level being 5. (See SAE levels of automation for vehicles.)

Vehicles similar to those manufactured by Nelix are being developed in the US. Perhaps most notable is the “mobile grocery store” supplied by San Francisco-based Robomart to retailer Stop & Shop.

Another notable delivery robot maker is Nuro, which makes a “self-driving vehicle for local goods transportation”, as the company describes it. The company is currently partnering with retailer Kroger to develop what they describe as “shopping delivery services”.

Almost instant delivery So this overview of the current technologies being developed and tested in the logistics and supply chain sector seems to suggest that a new last-mile delivery solution is emerging. This new solution – whatever its form – is unlikely to replace the human-operated delivery vehicle anytime soon, but it has already gained a significant market share and will continue to grow.

Additionally, last-mile delivery robots combined with warehouse goods-to-person robots are likely to make the e-commerce logistics and supply chain even faster than it is now.

At the moment, only some places in the world can offer to deliver an order with the “same day” or within “an hour or two”. It’s difficult to believe that this kind of timeframe exists let alone it being in the process of becoming the standard in the majority of places in the world, certainly in urban areas.

But that looks to be the way things are going, and it is opening up new areas of the market for existing companies as well as new and nimble startups of all kinds. l www.roboticsandautomationnews.com

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Recycling

Cleaning up in Japan’s demolition market Recycling: AMP Robotics and Ryohshin are partnering on automation for construction and demolition recycling

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MP Robotics, a pioneer in artificial intelligence and robotics for the recycling industry, and waste management technology company Ryohshin are to make and sell AI-driven industrial robotics for material recovery in the Japanese construction and demolition market.

The companies also announced the commercial launch of a high-performance construction and demolition robotic systems now available in Japan.

Ryohshin and AMP have co-developed a robotic system using the AMP Neuron AI platform to guide highperformance robots that recognise, sort, pick and process construction and demolition debris for recycling. The two robotic systems are called “AI-Benkei” and “AI-Musashi”.

AI-Benkei is the heavy-duty workhorse using a singlerobot cell to handle heavy debris up to 40 kg, processing up to 25 metric tons per hour.

AI-Musashi is the high-speed racehorse using a tandem-robot cell that picks smaller items at a speed of 160 pieces of material per minute, processing up to 10 metric tons per hour. editorial@roboticsandautomationnews.com

The two systems combine payload and speed to form a complete solution that can operate 24/7 and process materials including metal, wood, electronics, concrete and much more.

AMP has licensed AMP Neuron to Ryohshin for its use and sale of these robotic systems in Japan. In return, AMP will use the robotic technology developed with Ryohshin for its own next-generation AMP Cortex construction and demolition robotic system for sale in North America.

'Eyes' and 'brain' AMP Neuron is the 'eyes' and 'brain' of the robotic system achieving real-time pattern recognition to identify target materials. It continuously learns by processing vast amounts of data converted from millions of images captured via its vision system.

AMP Neuron recognises different colours, textures, shapes and patterns to identify material characteristics. It collects all data in a material stream, providing transparency about its material composition, as well as analysis of operational productivity.

Customers use this data to monitor and measure performance, while gaining critical insights to make key business decisions. l www.roboticsandautomationnews.com

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Top Features

AI chips

AI chip companies

AI chip companies: A macro step change inferred from the micro scale

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ne of the effects of the ongoing trade war between the US and China is likely to be the accelerated development of what are being called “artificial intelligence chips”, or AI chips for short, also sometimes referred to as AI accelerators.

AI chips could play a critical role in economic growth going forward because they will inevitably feature in cars, which are becoming increasingly autonomous; smart homes, where electronic devices are becoming more intelligent; robotics, obviously; and many other technologies.

AI chips, as the term suggests, refers to a new generation of microprocessors which are specifically designed to process artificial intelligence tasks faster, using less power.

Obvious, you might think, but some might wonder what the difference between an AI chip and a regular chip would be when all chips of any type process zeros and ones – a typical processor, after all, is actually capable of AI tasks.

Graphics-processing units are particularly good at AIlike tasks, which is why they form the basis for many of the AI chips being developed and offered today.

Without getting out of our depth, while a general editorial@roboticsandautomationnews.com

microprocessor is an all-purpose system, AI processors are embedded with logic gates and highly parallel calculation systems that are more suited to typical AI tasks such as image processing, machine vision, machine learning, deep learning, artificial neural networks, and so on.

Maybe one could use cars as metaphors. A general microprocessor is your typical family car that might have good speed and steering capabilities. An AI chip is a supercar, which typically has a more powerful engine and super-sensitive steering, and a lower profile for less wind resistance, and so on.

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AI chips

In other words, staying within our shallow depth of intellectual capabilities, we are led to believe that there is definitely a difference between AI chips and their conventional counterparts.

Microcontrollers, as many readers might know, are a type of chip which is built to do a very limited number of functions – operate a washing machine, for example, or an automatic door, or something like that.

Microcontrollers are sometimes called applicationspecific integrated circuits. Or rather, they used to be. But the ASICs definition appears to be relevant to a broader range of chips now. And AI chips may actually be classed in that category, since they are highly specialized.

But that is the extent of our knowledge about the technology. It’s a complex subject, with its own rich jargon, so simplifying is not that easy, especially now that things are changing. More as we get it.

AI wars Some might argue that the subtext of, or the really important issue in, the trade war between the US and China is indeed artificial intelligence.

It’s been said by many experts and commentators that whichever country can gain a lead in AI will be the most

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powerful economy going forward.

AI does, of course, have massive potential in the military, but we’ll try and stick to commercial and industrial sectors for now.

And we’ll also leave out quantum computing because that’s a few years away at least. But that, too, is another technology that the US is concerned about losing the lead in.

China’s progress in AI has been accelerated, possibly because of the government’s backing, but it’s difficult to see what politicians can do other than provide funding and other resources, along with propagandist proclamations.

If the funding and all the other things are available, as they are in both the US and China – and Russia, for that matter – it’s the technologists who, ultimately, will be the ones that make the progress.

On the point of Russia, it’s worth noting here that the country’s president, Vladimir Putin, has made comments on more than one occasion about the importance of AI. “Artificial intelligence is the future,” said Putin in a recently televised panel discussion. “Whoever becomes the leader in this sphere will become the ruler of the world.”

Putin’s words might sound somewhat bombastic to

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Artificial intelligence is the future.

Whoever becomes the leader in this sphere will become the ruler of the world.

Vladimir Putin

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AI chips

some people, but it’s difficult to disagree with his sentiments or overstate the power of AI. Most people, however, would probably prefer that the world moves towards a more diverse culture, a cooperative multi-culture, if you will, rather than one AI “god” ruling the world, as Putin appears to envision.

Or at least we would.

But that’s enough politics.

AI chips with everything So now onto the companies that we think are the top developers of AI chips, although not in any particular order – just companies that have showcased their technology and have either already put them into production or are very close to doing so.

And, as a reminder, we’ve tried to stick the definition of AI chips that we outlined at the beginning of the article, rather than just superfast conventional microprocessors.

In other words, these microprocessors listed here are being described as “AI chips” and are specifically designed for AI tasks.

And just to provide a commercial context, the AI chip market is currently valued at around $7 billion, but is forecast for phenomenal growth to more than $90 billion in the next four years, according to a study by Allied Market Research.

1. Alphabet Google’s parent company is overseeing the development of artificial intelligence technologies in a variety of sectors, including cloud computing, data centers, mobile devices, and desktop computers.

Probably most noteworthy is its Tensor Processing Unit, an ASIC specifically designed for Google’s TensorFlow programming framework, used mainly for machine learning and deep learning, two branches of AI. Google’s Cloud TPU is a data center or cloud solution and is about the size of a credit card, but the Edge TPU is smaller than a one-cent coin and is designed for “edge” devices, referring to devices at the edge of a network, such as smartphones and tablets and machines used by the rest of us, outside of data centers.

Having said that, analysts who observe this market more closely say Google’s Edge TPU is unlikely to feature in the company’s own smartphones and tablets anytime soon, and is more likely to be used in more high-end, enterprise and expensive machines and devices.

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Intel, the world’s largest chipmaker, was reported to have been generating $1 billion in revenue from selling AI chips as far back as 2017.

2. Apple Apple has been developing its own chips for some years and could eventually stop using suppliers such as Intel, which would be a huge shift in emphasis. But having already largely disentangled itself from Qualcomm after a long legal wrangle, Apple does look determined to go its own way in the AI future.

The company has used its A11 and A12 “Bionic” chips in its latest iPhones and iPads. The chip uses Apple’s Neural Engine, which a part of the circuitry that is not accessible to third-party apps. The A12 Bionic chip is said to be 15 percent faster than its previous incarnation, while using 50 percent of the power.

The A13 version is in production now, according to Inverse, and is likely to feature in more of the company’s mobile devices this year. And considering that Apple has sold more than a billion mobile devices, that’s a heck of a ready-made market, even without its desktop computer line, which still only accounts for only 5 percent of the overall PC market worldwide.

3. Arm Arm, or ARM Holdings, produces chip designs which are used by all the leading technology manufacturers, including Apple.

As a chip designer, it doesn’t manufacture its own chips, which sort of gives it an advantage in perhaps the way Microsoft had an advantage by not making its own computers.

In other words, Arm is hugely influential in the market.

The company is currently developing AI chip designs along three main tracks: Project Trillium, a new class of processors that are “ultra-efficient” and scalable, aimed at machine learning applications; Machine Learning Processor, which is self-explanatory; and Arm NN, short for neural networks, a processor designed to work with TensorFlow, Caffe, which is a deep learning framework, and other structures.

4. Intel The world’s largest chipmaker was reported to have been generating $1 billion in revenue from selling AI chips as far back as 2017. Actually, Intel is not currently the world’s largest chipmaker, but it probably was at the time.

And the processors being considered in that report were of the Xeon range, which is not actually AI-specific, just a general one that was enhanced to deal with AI better.

While it may continue to improve Xeon, Intel has also developed an AI chip range called “Nervana”, which are described as “neural network processors”.

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AI chips

Artificial neural networks mimic the workings of the human brain, which learns through experience and example, which is why you often hear about machine and deep learning systems needing to be “trained”.

With Nervana, scheduled to ship later this year, Intel appears to be prioritizing solving issues relating to natural language process and deep learning.

5. Nvidia In the market for GPUs, which we mentioned can process AI tasks much faster than all-purpose chips, Nvidia looks to have a lead. Similarly, the company appears to have gained an advantage in the nascent market for AI chips.

The two technologies would seem to be closely related to each other, with Nvidia’s advances in GPUs helping to accelerate its AI chip development. In fact, GPUs appear to underpin Nvidia’s AI offerings, and its chipsets could be described as AI accelerators.

The specific AI chip technologies Nvidia supplies to the market include its Tesla chipset, Volta, and Xavier, among others. These chipsets, all based on GPUs, are packaged into software-plus-hardware solutions that are aimed at specific markets.

Xavier, for example, is the basis for an autonomous driving solution, while Volta is aimed at data centers.

Deep learning seems to be the main area of interest for Nvidia. Deep learning is sort of a higher level of machine learning. You could think of machine learning as shortterm learning using relatively limited sets of data, whereas deep learning uses a greater amount of data editorial@roboticsandautomationnews.com

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gathered over a longer period of time to return results which are, in turn, designed to address deeper, underlying issues.

6. Advanced Micro Devices Like Nvidia, AMD is another chipmaker which is strongly associated with graphics cards and GPUs, partly because of the growth of the computer games market over the past could of decades, and lately because of the growth of bitcoin mining.

AMD offers hardware-and-software solutions such as EPYC CPUs and Radeon Instinct GPUs for machine learning and deep learning.

Epyc is the name of the processor AMD supplies for servers, mainly in data centres, while Radeon is a graphics unit mainly aimed at gamers. Other chips AMD offers include the Ryzen, and perhaps the more wellknown Athlon.

The company appears to be at a relatively early stage of its development of AI-specific chips, but with its relative strength in GPUs, observers are tipping it to become one of the leaders in the market.

AMD has been contracted to supply its Epyc and Radeon systems to the US Department of Energy for the building of what will be one of the world’s fastest and most powerful supercomputers, dubbed “Frontier”.

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AMD has been contracted to supply its Epyc and Radeon systems to the US Department of Energy for the building of what will be one of the world’s fastest and most powerful supercomput ers, dubbed “Frontier”.

7. Baidu Baidu is China’s equivalent of Google in the sense that it’s mainly known as an internet search engine. And like

www.roboticsandautomationnews.com

Features Google, Baidu has moved into new and interesting business sectors such as driverless cars, which, of course, need powerful microprocessors, preferably AI chips.

And to that end, Baidu last year unveiled the Kun Lun, describing it as a “cloud-to-edge AI chip”.

The company sees Kunlun’s application mainly in its existing AI ecosystem, which includes search ranking and its deep learning frameworks. But the chip has other potential applications in the new sectors Baidu is going into, including autonomous vehicles, intelligent devices for the home, voice recognition, natural language processing, and image processing.

8. Graphcore After listing seven relatively long-established companies whose main activities are not actually geared towards developing AI chips, we arrive at Graphcore, a startup company whose central aim is to build and supply AI chips to the market.

The company has persuaded the likes of BMW, Microsoft and other global names to invest a total of $300 million into its business, which is now valued at around $2 billion.

The company’s main product right now appears to be the Rackscale IPU-Pod, based on its Colossus processor and aimed at data centers, although it may create more with the mythical amount of money invested, as that’s where its future lies. The “IPU” stands for intelligent processing unit in this context.

9. Qualcomm Having made a ton of money through its association with Apple from the start of the smartphone boom, Qualcomm probably feels left out in the cold by the tech giant’s decision to stop buying its chips. But Qualcomm itself is, of course, no minnow in its sector and has been making some significant investments with the future in mind.

Last month, the company unveiled a new “Cloud AI Chip”, and appears to be linking it to its developments in fifth-generation telecommunications networks, or 5G. These two technologies are thought to be fundamental to building the new ecosystem of autonomous vehicles and mobile computing devices.

Analysts say Qualcomm is something of a latecomer to the AI chip space, but the company has extensive experience in the mobile device market which would be helpful in achieving its stated aim to “make on-device AI ubiquitous”.

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Thinci is developing hardwareplus-softwar e solutions for machine learning, deep learning, neural networks, and vision processing.

AI chips The Darpa-funded startup ran a successful Kickstarter campaign for its Parallella product and has raised more than $10 million in total investments.

Unfortunately, however, its website is currently difficult to access, which means we’re not sure what the status of their technology and company is, directly from the people concerned. But with so much interest in its technology, maybe they haven’t had the time to sort that part out.

11. Thinci Robotics and Automation News reported on this company towards the end of last year, when automotive technology supplier Denso led a $65 million investment round in the startup.

We described Thinci as a developer of autonomous car technology, which it is, but it’s also interested in the unmanned aerial vehicles, or drones, market.

The company says its technology is currently installed in data centers, but not much more is known about its products. What it does say on Thinci’s website is that the company is developing hardware-plus-software solutions for machine learning, deep learning, neural networks, and vision processing.

12. Mythic AI Having raised more than $40 million in funding, Mythic is planning to implement its “AI Without Borders” philosophy on the world, starting with data centers.

The company claims to have developed a method whereby deep neural networks no longer weigh heavily on conventional local AI because its system performs hybrid digital and analog calculations inside flash arrays, which it says is an “entirely new approach”.

Its GPU is capable of desktop computer performance but is the size of a shirt button, which means it can deliver

10. Adapteva This is one of the more intriguing companies on this list, not least because of the Parallella, which is sometimes described as the cheapest supercomputing system available.

Adapteva’s main AI chip offering is the Epiphany, a 1024 core 64-bit microprocessor which is said to be a “world first” in many ways. editorial@roboticsandautomationnews.com

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AI chips

“massive parallel computing” while being almost weightless.

All of which probably means its chips would improve the performance of edge devices without adding much weight or need for power.

13. Samsung Having overtaken Intel as the world’s largest chipmaking company, and Apple as the world’s leading smartphone company, Samsung is looking to create entirely new markets that never existed before.

One of those markets is for foldable smartphones, although the company seems to have suffered a hiccup in that area because some units broke in the hands of reviewers.

But even regular smartphones will need more AIcapable chips, so its work in that area will not be wasted.

Towards the end of last year, Samsung released the latest version of its Exynos microprocessor, which is designed for long-term evolution, or LTE, communications networks.

Samsung says the new Exynos is equipped for ondevice and enhanced neural processing units.

14. Taiwan Semiconductor Manufacturing Company Despite being one of Apple’s main chip suppliers for many years, TSMC is not exactly a boastful company. Sure, it’s got a website and updates investors with results, but it doesn’t talk much about its actual work.

Luckily, news media such as DigiTimes keep abreast of editorial@roboticsandautomationnews.com

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the goings-on at the chipmaker and recently reported that e-commerce giant Alibaba has contracted TSMC – as well as Global Unichip – to build an AI chip.

We don’t know much else about this, but given the size of Alibaba and the business connections of TSMC, the order is likely to bring about significant changes in the AI chip market.

15. HiSilicon This is the semiconductor business unit of Huawei, the telecommunications equipment manufacturer which is currently the subject of some indirect trade embargoes.

Basically, Huawei has been effectively banned from doing business in the US, and some European countries are now following America’s lead.

What this will mean for HiSilicon is probably too early to tell, but the company is not hanging around to find out.

It recently launched Kirin, which is described as an AI chip in some media, but we’re not so sure.

Anyway, it’s probably an early stage in HiSilicon’s AI chip capabilities and the company will need to accelerate its efforts if it is to offset the increasing number of supply bans Huawei is facing.

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HiSilicon is the semiconductor business unit of Huawei, the telecommuni cations equipment manufacturer currently the subject of some indirect trade embargoes.

16. IBM No list of this type would be complete without at least one mention of IBM, which, as you might expect, has massively well-funded research and development into all manner of technologies, many of which are related to AI. www.roboticsandautomationnews.com

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Groq believes that compute’s next breakthrough will be powered by a new, simplified architectural approach to hardware and software.

AI chips The company’s much-talked-about Watson AI actually uses what we’re calling conventional processors rather than AI-specific ones, but they’re quite powerful nonetheless.

In terms of specialized AI chips, IBM’s TrueNorth is probably in that category. TrueNorth is described as a “neuromorphic chip”, modeled on the human brain, and contains a massive 5.4 billion transistors, which sounds like a lot until you find out that AMD’s Epyc has 19.2 billion.

But it’s not all about transistor count and the actual number of components, it’s about how those components are used, and IBM is investing heavily in taking a central place in the AI chip landscape of the future.

17. Xilinx Talking of the number of components, Xilinx is said to be the maker of the microprocessor with the highest number of transistors. Its Versal or Everest chipsets are said to contain 50 billion transistors.

Moreover, Xilinx does describe Versal as an AI inference platform. “Inference” is the term which refers to the deductions made from the massive amounts of data that machine learning and deep learning systems ingest and process.

The full Versal and Everest solutions contain chips from other companies, or at least ones designed by other companies. But Xilinx is probably one of the first to offer such high-power computing capabilities to the market in self-contained packages.

18. Via Although Via doesn’t offer an AI chip as such, it does offer what it describes as an “Edge AI Developer Kit”, which features a Qualcomm processor and a variety of other components. And it offers us an opportunity to mention a different type of company.

It’s probably just a matter of time before AI is integrated into all the other low-cost, tiny computer suppliers, such as Arduino, Raspberry Pi, and others. One or two already pack an AI chip. Pine64 is one of them, according to Geek.

Apparently, it’s possible even now to develop AI

applications using Raspberry Pi and Arduino, but we’d have to refer you to Instructables or a similar website if you want more information about that. It’s certainly worth keeping tabs on the sector.

19. LG One of the world’s largest suppliers of consumer electronics, LG is a giant which seems to make some nimble moves. Its interest in robotics is evidence of this, but then, a lot of companies are looking to be ready for when smart homes let in more intelligent machines.

This website reported recently that LG has unveiled its own AI chip, the LG Neural Engine, with the company says that its strategy is to “accelerate the development of AI devices for the home”.

But even before it gets to the edge devices, it’s likely that LG will use the chips in its data centers and backroom systems.

20. Imagination Technologies Virtual reality and augmented reality use up more computing resources than almost anything else that runs on a device. Some of Google’s data center servers were said to have been brought to a crashing halt during the global craze over the Pokémon augmented reality game a couple of years ago.

So VR and AR probably necessitate the integration of AI chips in the data center as well as in the edge device.

And Imagination sort of does that with its PowerVR GPU. Imagination describes the latest PowerVR as a “complete neural network accelerator solution for AI chips” which delivers more than 4 tera operations per second, making it “the highest performance density per square millimeter in the market”, according to the company.

21. MediaTek Like most companies on this list, MediaTek is a “fabless” semiconductor company, meaning it doesn’t do the fabrication or manufacturing of the chips itself, just the design and development.

Its NeuroPilot technology embeds what it describes as “heterogeneous computing capabilities” such as CPUs, GPUs, and AI processing units into its system-on-chip products.

In this context, “system-on-chip” refers to the integrated circuit which connects all the components of a computing machine.

22. Wave Computing This company could probably be described as a specialist AI platform provider. Last month, it launched the Triton AI, which is another system-on-chip.

Wave says Triton is an “industry first” that enables developers to address a broad range of AI use cases with a single platform. It supports inference and training, and is flexible enough to support new AI algorithms. editorial@roboticsandautomationnews.com

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AI chips

Wave offers a range of AI solutions that go from edge devices to servers and data center racks.

23. SambaNova Having raised more than $200 million, this startup is very well resourced to develop custom AI chips for its customers.

Still in the early stage of its business, SambaNova says it is building hardware-plus-software solutions to “drive the next generation of AI computing”.

One of the company’s main investors is Alphabet, or Google. You’ll find that a lot of the big, established companies are buying into innovative new startups as a way to perhaps avoid being disrupted by them.

24. Groq This company was set up by some former Google employees, including one or two who were involved in the Tensor project, and is said to be rather low-key.

Crunchbase reported last year that the startup had raised $60 million to develop its ideas, which are founded on its stated belief that compute’s next “breakthrough will be powered by new, simplified architectural approach to hardware and software”.

Not much else can be gleaned from its website, unless perhaps you’re an expert. But expect to hear more about this company going forward.

25. Kalray This is a company Robotics and Automation News has featured in the past. One of its senior executives made a editorial@roboticsandautomationnews.com

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presentation and gave us an interview which can be viewed on our YouTube channel.

Essentially, Kalray is a well-funded European operation which appears to have produced an innovative chip for AI processing, in data centers and on edge devices. The company says its method enables multiple neural network layers to compute concurrently, using minimal power.

It appears to be particularly interested in intelligent and autonomous cars, which is understandable, given its close proximity to the giant German automotive industry.

26. Amazon Having practically invented the cloud computing market, with its Amazon Web Services business unit, it seems logical that Amazon gets into the AI chip market, especially as its data centers could probably be made more efficient through their integration.

The world’s largest online retailer unveiled its AWS Inferentia AI chip towards the end of last year. It’s still yet to be formally launched, but even when it is, it’s unlikely to be sold to outside companies, just supplied to Amazon group businesses.

27. Facebook Maybe we shouldn’t include this company on the list because it’s only just recently entered into an agreement with Intel on the development of an AI chip.

But Facebook has launched a number of innovative hardware products for the data center, so it’s probably worth watching what it does in the AI chip market. l

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