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.
Let’s talk about industries and about different sectors. So, which ones do you anticipate will be the most disrupted by AI technologies?
Regarding industries and sectors most likely to be disrupted by AI technologies, the imaging space is particularly promising, although it presents its challenges. For instance, the rise of Deepfake technology is a concern that needs addressing, but this also opens opportunities for companies to provide industries and governments with solutions for identification and recognition issues.
In commercial environments and for identification purposes, like border crossings, AI is already making an impact. At Schiphol airport in the Netherlands, for example, they’ve implemented facial recognition for check-ins, allowing passengers to proceed to their planes without repeated passport checks. This practice, which is also being adopted in America, significantly enhances the consumer experience thanks to AI.
The medical field, especially in cancer diagnosis, is another area where AI-driven imaging and data capture are making strides. Data centers are collecting vast quantities of medical images anonymously to maintain patient privacy, which gives doctors access to millions of images. This wealth of data can assist them in making critical decisions, such as determining whether a tumor is cancerous. The speed and support AI provides in this domain are why many doctors see it as the arena most ripe for advancement in the near future, with the potential to greatly ease their workload and accelerate patient care. This prospect is equally exciting for me within the medical space.
As AI intertwines with daily life, what kinds of applications do you believe will become ubiquitous?
I keep coming back to the medical space. We will see AI use expanding beyond clinical settings to patients at home, incorporating data, imaging, recognition, and security. This will allow patients to interact with hospitals and healthcare professionals much more easily, facilitating quicker and more efficient diagnostics. This is particularly important for patients who may have difficulty getting to the hospital, but it will also transform interactions within the hospital environment.
In the travel industry and with transportation mobility, many changes are anticipated as well. Image recognition and identification will streamline and secure access to services.
The payment industry is also evolving rapidly. We’ve gone from cards to tap-and-pay, and now we’re using our phones. Payments are beginning to rely on biometric verification like fingerprints and facial recognition. As we become more comfortable with the reliability of these images, we’ll see significant advances in payment processing and ID verification for things like border crossings.
We’re already witnessing trials in supermarkets, particularly in the U.S., with companies like Google and Amazon experimenting with shops where you register your biometrics, pick up items, and simply walk out without a traditional checkout process—your payment is processed automatically. It’s a bit strange now, but these systems are becoming more familiar as we get used to them.
And as we look towards a future with intertwined with AI, any closing thoughts on how Axelera AI and similar ventures can really drive meaningful impact in this landscape?
Yes, we touched a bit earlier on ecosystems and the burgeoning growth of different applications and use cases at the edge.
As Axelera AI continues to grow, particularly in the imaging space, and leverages these ecosystems to their fullest potential, it will naturally engage in a broad spectrum of activities, such as robotics and related imaging tasks. By expanding its focus and collaborating with other companies within these ecosystems, Axelera AI significantly broadens its horizons beyond its current scope. And we don’t know where that will take us. This growth could extend to industrial, medical, or commercial environments, as well as security, among others. Diverse applications are intrinsically connected, and it is the ecosystem itself that will chart the course forward.
From my fifteen years of experience, I’ve seen that engaging with an ecosystem can accelerate business growth by three or four times compared to trying to forge a new market on your own. Axelera AI recognizes this and is moving in that direction rapidly.
It’s exciting times ahead, Stephen.
Yes, I’m surprised at the pace at which it’s picking up now, but we really shouldn’t be because there’s always a slow startup before a market takes off, and then it extrapolates. I think we’re in that phase now.
AI is a buzzword for many people. Once we delve into the real use cases and how we can take advantage of it, it’s going to be a super-growth market over the next few years.
Looking at companies that are growing and their performance on the stock market, you can see that there’s a lot of excitement and attention being paid to those involved with AI.
And I believe that’s where most of the investment is going to flow into in the short to medium term, which is fantastic.