Introducing Stephen Owen, Axelera AI Advisor

Axelera AI has recently welcomed Stephen Owen as an Advisor. Stephen is a highly experienced executive with over 16 years of board-level experience in an S&P Top 500 semiconductor company. He has extensive knowledge in global leadership, organizational management, sales, and marketing. Stephen successfully led a global team of over 1800 employees, consistently delivering exceptional results that significantly boosted the company’s performance over several years. He formerly served as the Global Marketing and Sales EVP at NXP Semiconductors.

Read or watch the interview to learn more about his insights and experiences.

Axelera AI is focusing on the imaging space, which has a huge market and significant potential.

You’re a seasoned executive with extensive experience in the high-tech industry. What sparked your interest in the AI sector and let you to join Axelera AI as an advisor?

In my previous roles over the last few years, we began to explore AI, machine learning, and the use of algorithms to improve a variety of different areas. Administrative tasks, for instance, but what’s more interesting is the application in vehicles—machine learning aids with infrastructure, secure edge, secure IoT.

Also, in the sales department, we tried to understand how customers engage with us as a tech company. We aimed to create a database that could interact in such a way that we could serve not only 500 direct customers but also 50,000 indirectly online. It’s a massive challenge—you can’t possibly staff enough people for that. You need to move that interaction online, and to provide meaningful responses, you have to employ AI. So you build this database of questions and answers and refine the system with keyword searches, among other tools. And that’s what we built upon.

Technically, we’ve applied various innovations related to microprocessors. This is where my interest peaked, especially when I began to connect with the people at Axelera AI, which then led me to look beyond just processing to AI and cloud services and so on.

From your perspective, what are the most compelling opportunities and challenges facing startups in the AI solutions space today? And how is Axelera AI positioned?

The biggest challenge for many companies in the AI space, such as Axelera AI, is deciding what to focus on first. AI is a pretty generic term, and there are so many different directions you could go in.

Axelera AI is focusing on the imaging space, which has a huge market and significant potential. Then, there are the machine learning opportunities, such as predictive maintenance programs.

The medical space is also an exciting opportunity, intersecting with imaging. It involves looking at mammography, X-rays, scans, and using AI to detect tumors. Predictive maintenance for hospitals, as well as in robotics and industrial environments, can save millions of dollars. Predicting breakdowns well in advance saves a considerable amount of time.

On the imaging front, the possibilities are endless for how it can be used, from identification in a commercial or retail environment to distinguishing between bad and good actors. The potential extends even beyond that.

The key is really about focus. Don’t try to do everything for everyone. Pick your markets. Axelera AI is doing just that, concentrating on imaging.

For companies like Axelera AI, the opportunities are indeed fantastic. But the key is really about focus. Don’t try to do everything for everyone. Pick your markets. Axelera AI is doing just that, concentrating on imaging.
 

How do you envision the transition from cloud to edge computing altering the broader technological landscape?

For over a decade, we’ve anticipated the proliferation of IoT nodes, and now we’re seeing it come to fruition, especially during the COVID period, with an uptick in devices connecting from homes and offices. This increase has pressured infrastructure to manage higher data rates, something traditional data centers alone can’t handle. The evolving solution involves super powerful data centers performing the heavy computing, then distributing that data back to local environments. The capability of local nodes—devices at the edge—has greatly improved. They can now handle more complex tasks, including their own AI calculations, reducing the latency issues associated with data constantly moving to and from the central data centers.

A crucial element in this transition is addressing security. Secure edge computing is essential for protecting the vast amount of data generated at the edge and in the cloud. We’re going to see a shift towards more localized system operations, which is where the real work happens and is most needed.

In the automotive industry, for instance, it’s impractical to rely on data centers far removed from the action. Processing needs to happen on-site, rapidly and efficiently, to recognize and respond to situations. The same principle applies to retail and other industries—speed and local processing are of the essence.
 

With the increasing shift towards edge computing and the strain on data centers, how do you foresee companies adapting their infrastructure strategies to optimize performance and efficiency in this evolving landscape?

The primary shift will be to delineate the two domains: the data center and the edge computing sectors. Introducing more potent accelerators into the mix is where organizations like Axelera AI become pivotal. With high-end computing platforms and AI accelerators integrated into systems, edge devices will gain significantly more computational power. This will enable them to handle more complex computations and use cases that were previously unattainable.

This evolution implies that companies will need to collaborate to create a cohesive system. They’ll need to work in unison to run AI models and think strategically about constructing infrastructure that functions as a unified entity. That’s one of the formidable challenges today.

Traditionally, companies have operated their own infrastructures and solutions in isolation, but there’s a gradual shift happening. As the necessity for cooperation becomes more apparent, startups are recognizing this need. Increasingly, larger corporations are also starting to acknowledge this trend and are beginning to transition as well.
 

So you say it’s it’s a shift becoming towards more of the ecosystem?

Of course, the movement is indeed towards a more ecosystem-oriented direction. This is exemplified by the alliance of consumer electronics companies with chip manufacturers in creating the Matter standard. Matter enhances plug-and-play capabilities, making it easier for devices to work together out of the box, and even competitors are providing solutions that are interoperable.

This ecosystem approach, designed for consumer benefit, is also essential in larger scale systems such as edge computing, security, and AI. These sectors require companies to collaborate, creating models and systems that can operate together efficiently across various platforms, whether it’s RISC-V or ARM-based systems, or accelerators designed for specific architectures.

Ultimately, the AI software that powers these systems must be neutral and flexible, able to operate across different hardware environments. This neutrality ensures that AI can be a versatile tool, capable of being implemented in multiple ecosystems, serving the broader purpose of enhancing consumer convenience and experience.

What emerging trends withing AI and edge computing excite you the most?

It’s a big list,

My focus, particularly in the medical arena, is on advancing women’s health. It’s a comprehensive field where significant progress is needed and where I believe we can make meaningful advancements.

In the realm of industrial automation, we’re witnessing a shift towards what are known as ‘dark factories.’ These are spaces where fewer people are needed, and the factories can operate in the dark because the machines and robotic systems take over. This allows us to reallocate human resources to other tasks and increase overall efficiency.

Furthermore, automotive and mobility represent another area ripe for innovation. With the amount of traffic congestion globally, there’s an enormous amount of carbon fuel wasted. If we can develop an infrastructure that interlinks homes, cars, trains, buses, scooters, and more, and streamline it with better data, we can save significantly. Not only in terms of fuel but also by making our cities safer and more efficient places to live.
 

It’s about connecting everything.

Yeah, But it’s utilizing that connection. And taking advantage of the fact that there is such a big connection of so many systems and then concentrating on how to make those systems and ecosystems work together and get the maximum efficiency out of it.
 

How do you think open-source architectures like RISC-V are influencing the dynamics of the AI semiconductor industry?

RISC-V is making quite an impact on the AI semiconductor industry. It has been a refreshing development, allowing many startups to expand their businesses more rapidly. Additionally, it has offered consumers and customers a new perspective, a different way to look at their computing needs.

Alternatives are incredibly important in this market. From what I’ve seen in my experience with bringing products to market, customers can become concerned when there is only a unique system available without alternative options.

With RISC-V’s expansion, more semiconductor companies are considering providing solutions that include both RISC-V and ARM. This indicates that RISC-V is poised to have a significant future alongside ARM.

Companies like Axelera AI are taking advantage of this opportunity. They are focusing on RISC-V and ensuring that their software can operate in both the RISC-V and ARM ecosystems. This adaptability is a major advantage that can help make customers feel more at ease. It also presents a great opportunity for Axelera AI to attract new customers.
 

Despite the perception of fragmentation, do you see a burgeoning market for AI at the edge, especially compared to cloud-based solutions?

It really depends on whether you’re considering pure software applications or those that are tied to real-world, local functionalities. Take, for instance, camera-based or imaging-based systems where Axelera AI is likely focused—these are inherently at the edge.

Data predominantly will end up being sent back to the data center, but a substantial amount of processing occurs at the edge. Immediate reactions and responses, whether in security or retail environments, are handled locally and in real-time.

We’re observing a proliferation of systems at the edge, which is crucial. More companies are moving towards edge computing, realizing that their systems become more efficient and interactive, with the ability to perform transactions and interactions much faster, all thanks to secure edge computing. AI is instrumental in accelerating this, as data doesn’t just go back to the data centers. A lot of the work happens at the edge, where information can be consolidated, and machine learning models can be further refined before being pushed back to the edge.

So, while there’s a valid place for cloud-based solutions, the applications at the edge are numerous. Many companies, when they direct their focus appropriately, will likely see their businesses grow.