Beyond the clouds: Bringing the power of edge AI to the far reaches of society

• Edge AI takes a step beyond cloud computing, bringing processing back on-site
• Edge computing is predicted to dominate more than half the total AI market by 2030
• Harvest Tech and other players provide a local lens through which to view edge AI and how it could transform automation, operations management and more

A new era of artificial intelligence

Artificial intelligence is becoming so ubiquitous it almost seems like a meme.

It feels like every second day some new version or fresh application of AI is being splashed across headlines or spruiked by the latest tech start-up hopeful.

Large language models, agentic algorithms, cloud computing, generative AI… it’s more than enough to make your head spin.

“GenAI is evolving at a breakneck pace, pushing the boundaries of AI capabilities in economics and finance and introducing novel solutions to old challenges,” International Monetary Fund head of digital advisory Herve Tourpe said.

“Governments are beginning to employ these smarter tools to improve citizen services and overcome workforce shortages,” Tourpe explained, adding that central banks are taking note, too.

While it could take several years before we figure out exactly how best to use this exciting and confusing new frontier of technology, we’re already imagining far more interesting and paradigm-changing applications for AI than banking watchdogs.

Out of the clouds, onto the edge

Most AI requires cloud computing to function.

The ‘cloud’ is a network of computers and servers all connected through the internet, which are able to act like one big repository of data and processing – like a super computer spread over dozens or even hundreds of devices.

Artificial intelligence requires a ridiculous amount of processing power, especially at the learning and model-building stage.

Singular supercomputers capable of coping with such extreme levels of data processing are incredibly expensive, not only to build but to power and maintain.

Even the smallest supercomputers cost tens to hundreds of millions, but leading AI-capable units run in the billions.

Cloud computing takes care of that problem – it allows the load to be shared across a whole network of computers, reducing cost and increasing flexibility.

Unfortunately, cloud computing has its own limitations.

It requires high-quality internet connections with large bandwidth and low latency, capable of transferring large chunks of data rapidly and reliably.

For intensive AI applications, they also need to be connected with data centres, which act like information hubs, data storage and highway controllers between the networked computers.

Again, a strong internet connection is absolutely vital. You simply can’t run these processes without the infrastructure to handle it.

That’s where edge computing comes in.

Edge AI takes advantage of a separate technology – known as the internet of things – to overcome individual device processing limitations.

Rather than relying on the internet, edge AI brings together a collection of smaller devices – cameras, tablets, computers, micro data centres, communications equipment – connected on a local network, and uses their combined data capture and processing power to apply AI technology.

The idea is to bring all that data back to the source, enabling both AI algorithms and on-site operators to make split-second decisions in real-time.

Autonomous vehicles, industrial automation and healthcare monitoring all use edge AI.

You certainly wouldn’t want your self-driven taxi pausing for multiple minutes to decide whether to enter an intersection while it waits for a response from a server 100 kilometres away.

That’s all got to happen within the car itself instantly, or the AI simply can’t fulfil its function as intended.

Edge AI promises to bring intelligent processing directly to where it’s needed most, at the coal face.

Are we there yet?

Less than a week ago, a Tesla drove itself from Sydney to Melbourne with only passive supervision from a human driver.

Waymo, the self-driving taxi service based in the US, is expanding its fleet of autonomous taxis to 3500 vehicles by 2026.

Kodiak Robotics is pushing into the long-haul autonomous trucking sector, coaxed along by a US$50 million grant from the US Department of Defense.

For all that, edge AI computing is still in its infancy.

The technology is undergoing rapid, almost daily advancement, but the challenges are many-fold.

“Creating something that’s fit for purpose and delivers value to customers at an attractive price point is a real technical challenge,” Harvest Technology (ASX:HTG) chief product officer Damiain Brown told Stockhead.

Harvest Tech is an operations solutions technology company, specialising in secure and stable remote and off-shore applications.

The company’s main offering is the Nodestream Protocol, a remote monitoring and operations product that offers huge bandwidth efficiency and connection stability advantages over traditional operational solutions.

“Our customers range widely. Some companies want AI in everything but have no budget, while defence contracts might have very deep pockets but very specific standards to meet.

“It’s difficult to design an agnostic system that will be fit for both purposes.”

Going fully robotic

Harvest Tech dreams of facilitating fully autonomous remote worksites via its Nodestream protocol, and the company is pushing strongly into edge AI within its Nodestream Enhanced Operating Network (NEON) development project to facilitate that vision.

“We work in really remote offshore sites. They’re disconnected. They don’t have the networks and everything else that people who operate in metropolitan areas have,” Brown explained.

“The main NEON platform will work around intelligent monitoring of remote sites, with a set of actions and commands the system can enact on top of that.

“You can think of it like a digital watchdog linked with a concierge intelligent enough to respond to incidents.”

Harvest imagines deploying intelligent operational incident monitors capable of not only recognising emergency situations, but deploying alarms, response systems and alerting the necessary stakeholders in real time, even in the most remote locations.

The technology could respond to incidents on unmanned vessels, enable firefighters to communicate and coordinate deep within inaccessible mountain ranges, or even monitor pollution and fishing nets via millions of individual deep-sea sensors.

That’s the goal, but there’s still a lot to accomplish before the technology can be fully realised in these ways.

Meanwhile hundreds of thousands of individual companies are developing proprietary offerings in the edge AI space, all racing each other to be the next big thing.

“There are so many versions of this technology, [so when] choosing which one to integrate for a broad swathe of industries and applications – everything from hydrocarbon production to first responders or defence – being agnostic is really important,” Brown said.

“We’ve also got to future proof. A lot of what’s available today will be legacy within weeks or months.

“This technology is just moving so damn quick.”

Who’s doing the leg work?

While Harvest Tech works to bring its end-use vision of autonomous deep-sea rescue missions and fully remote oil rig repairs to life, a swathe of other ASX-listed companies are working on the foundational technology to facilitate it.

Brainchip Holdings (ASX:BRN) is quite literally developing the chips that could power Harvest’s edge AI goals.

A self-described ‘worldwide leader in edge AI on-chip processing’, Brainchip is developing its Akida technology to mimic a human brain, analysing sensor inputs at the point of acquisition (the chip itself) and processing the data on the spot.

BRN’s core offering is the Akida AI Acceleration Processor, a low-power, real-time AI processing chip designed to facilitate vision, audio and sensor functions.

It’s use applications include things like vehicle health monitoring via engine noises, vibrations and brake wear, and radar and LiDAR-based low-visibility perception for autonomous vehicles and similar technology.

Still on the chip architecture front, Weebit Nano (ASX:WBT) is developing a new class of memory chips called ReRAM, which promise to overcome current chip limitations in terms of power consumption, speed, endurance, cost, and size.

Resistive random-access memory (ReRAM) was one of the centre pieces of the 2024 TSMC Technology Symposium, positioned as a Flash memory chip replacement and a leading contender for machine learning applications.

The technology is incredibly technically advanced, but it boils down to being able to squeeze a hell of a lot more data onto much smaller chips, with lightning-fast data access at a fraction of the cost.

Weebit isn’t the only one developing ReRAM, but it’s managed to create a bit of space for itself by designing its chips with standard materials and tools, meaning they can be manufactured in existing plants with very little investment or equipment changes required.

Moving away from chip architecture, DXN Solutions (ASX:DXN) has a novel solution for edge computing needs – miniaturised, modular data centres (DCs).

Rather than concentrating processing power in massive, power-hungry, centralised DCs, DXN is building bespoke, prefabricated Edge Data Centres.

Specifically designed for Edge computing, DXN offers custom-sized, modular data centres of all sizes, ranging from a simple broom closet-sized two-rack micro data centre, to 50-rack, warehouse-sized DCs.

DXN has already inked several data centre delivery deals, including with major gold miner Newcrest Mining (ASX:NCM) and international construction contractor Multiplex.

The company has deployed its edge AI solution to more than 30 locations across Australia, the South Pacific and Africa, servicing companies like Boeing and Anglo America.

That’s just one, relatively small ASX-listed company – uptake of Edge AI has been incredibly fast, growing at an incredible rate in terms of market penetration.

SHD Group, a global technology and automotive-focused market analysis firm, predicts edge AI will penetrate more than 31% of the greater market by 2030, foreseeing revenues of US$100 billion by the same year and a massive 55% capture of the overall AI market.

On the edge of tomorrow

So, the technology isn’t quite here yet, but at current rates of progress it promises to arrive at a blistering pace.

That’s part of the challenge for companies such as Harvest Tech, which are deliberately reaching just a little further than our current capabilities.

“We’re trying to find the thin edge of the wedge and build for tomorrow,” Brown explained.

“We’ve got to be very careful not to paint ourselves into a corner. Obsolescence is a real concern.

“Once, you’d build something and it would be fit for purpose for years. Now, it could be disrupted and obsolete within weeks.”

In other words, tread carefully, and don’t trust the PR spin. Things are moving far too quickly in the edge AI space for anyone to get too comfortable.

That said, all signs are pointing to a far more connected, efficient and autonomous future just over the horizon.

“Edge AI is the ultimate and probably only scalable way to do AI in the real world – collecting, analysing, and acting on data where it lives,” Qualcomm senior director of AI Research Evgeni Gousev said in an interview with Schneider Electric.

“In our industry, with the way things progress now, one day equals a year of development,” Gousev said at the tinyML Innovation Forum in Milan.

“It’s like a snowball effect.”

TinyML Foundation executive director Pete Bernard said that while Edge AI isn’t as exciting or glamorous as GenAI, it’s having a real impact on people’s lives.

“The value proposition is pretty universal,” he said.

“Whether you’re using it to fix a water system or grow better crops or have better health outcomes, its lower cost, lower power requirements … people can get their hands on this stuff and just start building.

“Every Fortune 500 chief technology officer right now has an AI strategy, and I suspect edge AI is going to be part of that strategy.”

At Stockhead, we tell it like it is. While Harvest Technology is a Stockhead advertiser, it did not sponsor this article.

This article does not constitute financial product advice. You should consider obtaining independent advice before making any financial decisions.