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Christina Cardoza: Hello and welcome to “insight.tech Talk,” where we explore the latest IoT, edge, AI, and network technology trends and innovations. I’m your host, Christina Cardoza, Editorial Director of insight.tech, and today we’re going to be talking about AI partnerships that spark developer engagement and innovations.

Who better to discuss this with than two companies embedded in the AI and developer communities. Today we’ll be speaking to Paula Ramos from Intel as well as Jason Corso from Voxel51. But as always, before we get started, let’s get to know our guests. Paula, a good friend of the show; for those of us who haven’t heard your previous conversations, what can you tell us about yourself and what you’re doing at Intel?

Paula Ramos: Yes, for sure. Thank you, Christina, for having me here. So, I’m so excited. So I, Paula Ramos, I have a PhD in computer vision and machine learning, and I’m working at Intel as AI Evangelist, working with multiple products and multiple developers around the globe.

Christina Cardoza: Great. And Jason Corso from Voxel51, first-time guest of the podcast. What can you tell us about yourself and Voxel?

Jason Corso: Likewise. Nice to meet you all. Thanks for the invitation, Christina. So, Jason Corso. Yeah, I have a PhD in computer science. I’m a Co-Founder at Voxel. At Voxel we make a software refinery to help you work with your data, your models, various needs, and kind of refine them into production visual AI.

I’m also on the faculty of robotics and EECS at the University of Michigan, where I’ve done research for the last 10 or 15 years in computer vision and machine learning, all at the boundaries between the physical world and what we can do with computational systems these days.

Christina Cardoza: Awesome. So you’ve been in this space for a long time now and have probably seen it evolve even—it feels like every day something new is happening, and it’s evolving even further. So that’s where I wanted to start off the conversation with you, Jason. If you could just talk about what you’re seeing in this space, how it has changed over the last few years, where we are today, and what are the trends shaping where we are.

Jason Corso: Yeah. Indeed, it has changed quite a bit in the last, even the last few years, also the last 20 years. Like when I was doing my PhD, we were looking at things about how you can use computer vision to understand gestures and so on to interact with the computer, and look where we are now, right? 20 years later it’s been quite a wild ride.

So last few years, let’s see. I think there probably are two major developments I would argue in the last couple years that really are driving the way we all think about AI. So the first one is probably pretty obvious, right? Like the availability of these large language models that capture huge token lengths and can embed actually natural human language into the language model that’s there to really give us a resource in which we can interact with rather naturally.

Now, I mean, there are an awful lot of questions around what their limitations are and their capabilities are, but at the same time I think it’d be easy to find lots of different applications, right? I think in the beginning of this year I wrote some quick note on LinkedIn about how I think LMs will evolve in this coming—in 2024, this year. One of those key elements that I thought was that we would see a true revolution in how we think about search—and just information search, information gathering, and all that and so on. And I think we really are beginning to see that.

I think on the other one, though, I’d probably point to an appreciation for the role that data has begun to play or has been playing in the development of various AI ML models. Everyone when you go to school, in grad school, you take your machine learning course, and you go and start training models to recognize digits and so on. You just go quickly download a data set, either it’s from some repository or your professor gives it to you, and most of the focus is on the algorithm.

And so we’ve built this culture of the model is king. But if you really think about what’s happening, even various leaders in the LLM space—to bring back to the first one—have begun to talk about the critical role that data, good data, high-quality data plays in this marriage of model, code, and data to build the AI systems that we’re using.

So I don’t know exactly where that appreciation is going to lead us. At my company, for example, we focus heavily on the role that data plays and providing developer tools for engaging with data alongside their models, rather than just expecting you to gen up some scripts to visualize your data or whatever, right?

But I think it’s good for me, because it’s a long time since when I was—like, 20 years ago my data sets were dozens of samples, hundreds of samples, right? Now we have data sets that are dozens of millions of samples or whatever. So actually managing them and understanding the failure modes and the distribution and so on is very difficult and requires, I think, new thinking.

Christina Cardoza: Yeah, absolutely. And you mentioned the search and information gathering. I’m definitely seeing on the consumer side AI being more prominent in these areas. When I search on Google or anything now, instead of just getting a list of links, an answer from Gemini comes up.

So it’s interesting to see how AI is evolving, but I’m glad you brought up LLMs, the repositories, and algorithms, and this data, and these models, because it’s really the developers that are pushing these advancements forward. A lot of times on “insight.tech,” we’re writing about advancements in manufacturing and retail and education, how businesses are using AI to transform their spaces; but what’s behind these transformations are really developers that are building these solutions that are working with LLMs.

So, Paula, I’m curious from your take, because you work with a lot of developers, you talk to a lot of developers in this space, what has their role been in keeping up with AI? And how can they even continue to compete in this space with all of the advancements and skill sets happening?

Paula Ramos: Yeah, that is a great question. I think that all of the developers are looking for their path every day because the things are changing so fast. But the main things that we need to have in mind as the developers—what kind of challenges we have—is that we need to drive innovation in a huge field that is there: artificial intelligence. So we need to be creative, we need to build intelligence applications, and we need to solve problems.

Maybe we have the same problems that we had 20 years ago, as Jason was mentioning, but we have better tools right now. We have a better way to approach those solutions, but we need to be so creative with those solutions. So, still we have a lot of tools, and we need to think about the final user of the applications.

So I think that there are some challenges right now in still we have room to improve: that is model development, data management, or how we can deploy those models in the easy way. We need to use a cloud system, or we can use an edge solution. We need to think about, independent of that, for sure, the skills that we need to find could be different, but basically having developers programming in different kind of languages, organizing or producing different kind of data sets.

Also something that is really important in this field is the open source community. Open source community is changing the cadence of the AI, because when we have these models open to everyone, they can access those models. So they can access those data sets and improve and improve those models round by round of those data sets, round by round.

So I think that the responsibility that we have as a developers is huge in this new era of AI. For sure, I think roles are in different kind of sectors. We can talk about manufacturing, retail, but more than that is what kind of problem we want to solve today. Could be complex, could be simple, but the solution always will be the simplest as possible, and this is the main challenge that developers have right now.

Christina Cardoza: Yeah, I love how you said we need to drive innovations, we need to create intelligent applications, we need to solve problems. Because developers aren’t in it alone; they don’t have to build it from scratch. They can leverage partners like Intel and Voxel and community members to make some of this happen.

For instance, I love that Intel has the Edge Reference Kits, and sometimes you guys are walking them through how to build a solution and giving them the code to do self-checkout or to build something in manufacturing, and they can just customize it after they learn a little bit more about it and how to do that.

So I’m curious, in what other ways can developers partner with companies like Intel, and how that’s going to benefit them to reach out into these different areas and to ask for help or ask questions and be a part of the Intel community or other open source communities?

Paula Ramos: That’s a great question. So, we have multiple channels right now. As you mentioned, we have the Edge Reference Kits that developers can access. In an easy way they can find a solution—complex problem with an easy solution—where we are trying to show them with tutorials, code, videos how they can navigate that specific vertical: manufacturing, retail, healthcare, LLMs as well, and working with multiple models.

Intel has a variety of solutions. Basically we have solutions—we have hardware accelerators for retraining, for fine-tuning models, but also we have solutions that work at the edge. Or also you can use your laptop—you can use your laptops to work with AI. And we are creating a specific framework that is called OpenVINO, where developers can use OpenVINO to optimize and quantize a model. That means that they can use the same infrastructure that they have, they can use the same computer, and they can run LLMs, optimize and quantize LLMs—INTEGER*4, for example—or they can use the integrated GPU that Intel also provide the processors.

I think that Intel with OpenVINO is enabling developers to easily prove and test these LLMs. And this is just one step behind the real solution, the solution that we want to put in the production systems. So they can create pilots; they can impress the bosses with the tutorials and examples that they can run in their own laptops before moving to the real or the final production system. And Intel has this possibility also. Developers can access Intel Developer Cloud to test multiple hardware before to buy that hardware. That is really cool. And also accessing accelerators and accessing also the latest, for example, AI PC.

So we are provisioning a lot of tools to developers, and also we have—I almost missed that—but we have an amazing repository where developers can test the latest AI trends. So we have OpenVINO notebook’s repository, where if something happened today, literally in two days we will see the notebook with that specific model, for sure. This is for the open source community. So you can test there, for example, Llama 3.1, YOLOv10, and the latest AI trends. And this is a great tool.

And the most important thing is we are not forcing developers to buy specific hardware to run those models as well, so developers can also run these models in the actual hardware that they have. We are also supporting ARM, and we are also supporting a variety of Intel hardware. Also integrated GPUs—that is the most usage, an integrated GPU—that we can see in the world.

Christina Cardoza: Yeah, it’s great that you are making it easy for developers to get started with the equipment or hardware that they have. And a lot of the kits and the challenges we were just talking about and repositories—these are ongoing things that are available to developers at any time. But I’m thinking about—I know recently, which probably feels like forever ago, you were at CVPR, and there was a competition and challenge going there. So that’s more of a one-off, timely challenge that is available sometimes to developers going to these events, having these things happen.

So I’m curious, Jason, because I know the company was also at that event, but there’s other events that you guys host or that you’re at that have these developer challenges. I’m curious, what would you say is the importance of developers going to these events, engaging in these communities, and participating in some of those competitions?

Jason Corso: Yeah, it’s a good point, right? I mean, even just before CVPR, Voxel had our first in-person hackathon, actually in New York City, and it’s that type of engagement where we really see excited developers engaging with new technology and then really trying to work together on new teams of solve a new problem.

That was really fun, but I think one key angle for developer events is obviously education, right? Learning new things. And I think if you take my earlier answer about how AI has evolved and think about a key trend for the future, a key trend that we’re seeing for the future is language combined with vision combined with new compute capabilities and openly available data, and these foundational models to really tackle new problems in what at Voxel we call visual AI.

I think we’re going to see increasing contributions to that effect, but how do you do it? What do you do? One has to go to developer events or other types of conferences like CVPR or whatever, truly, to really stay abreast of what’s happening there. I mean, for me it’s, in some sense, the educational side is very natural, right? I’m a faculty member; I teach. I’m not teaching right now this year, but last year I taught intro to computer vision. So three hours a week I was doing this developer event, in some sense, for 300 students to learn about computer vision.

So I think one thing we’ve learned at Voxel is this AI space is evolving so rapidly that it seems like everyone—even faculty members who’ve been in the field for ages—we’re in constant information-gathering mode. It’s impossible to stay up to date with everything from cutting-edge research papers on one hand, all the way to what are the new APIs and libraries that you have to then, that you have to learn.

And so to do this, at least at Voxel, what we’ve tried to do is maintain a weekly—at least one per week, if not more per week—sort of technical output that in some form of an event, like different formats, that really allows the community to stay engaged. So we have an events calendar at voxelv51.com that we can include in the show notes. I think we have something like two dozen events scheduled between now and the end of the year.

Just personally, for example, every Monday at noon Eastern I maintain these open office hours where anyone can sign into them—they’re on Zoom. We talk everything from—a couple weeks ago we were reviewing someone’s paper, and we went through slides and an actual technical model. All the way to like—oftentimes I get asked, “This is my first time thinking about getting into computer vision. What should I look at first?” Right? So, pretty broad. But we have some hackathons, virtual meetups, and so on. So I think that it’s like raw education just about foundational capabilities, but also these developer events really help engagement just from staying up to date with what’s happening.

Christina Cardoza: That’s great, and that’s awesome that you have those open hours that developers can just join and start to learn. I’m curious, because obviously there are virtual conferences, then there can be conferences in different parts of the world, and it can be tough for developers: they can’t go to all of them or there’s just so many out there, it’s hard to choose from. Is there anything coming up that you want to call out that developers should have on their radar? Or is there anything, any other resources available to them online, that you think that they should take advantage of?

Jason Corso: What Paula was saying earlier, being open source is like the gateway to fostering innovation, right? Like our software at Voxel51 is called FiftyOne. It’s on GitHub. We have the permissive licensing for the open source component of it, which is basically one user, one machine local data. You can fork it. You can submit PRs. We make releases—I think it’s on the order of every one to two months. Every release that we have has some content from our community, and we’ve been educated so much over the last four years since we released it about—from community needs and community contributions.

Most recently we have this new functionality called Panels, which—FiftyOne is basically a visual component as well as a software SDK for doing the work that we’re talking about here, like data and model refinement. But with Panels you can build functionality for the front end without knowing how to write React or JavaScript or anything with UX. You can write it right in Python, and all of a sudden you can still enhance the GUI functionality.

So I think those are great ways—actual events, but also just becoming a part of open source projects is another way to really to get involved in the developer ecosystem for AI.

Christina Cardoza: Yeah, absolutely. And I think it also helps, companies like yourself who have these open source models. You might not have picked up on something that somebody in the developer community picks up on, and they can really be a part of that community and make changes and point things out and contribute to companies and projects like yourself. So it’s always great to be a part of those discussions, see what’s going on, hearing what developers are talking about, as well as some of the ongoing challenges that they’re facing in these spaces.

Paula, I know OpenVINO—there’s a huge GitHub community around there, and you mentioned a little bit of the kits and some other things that Intel offers, but I’m curious, in what other ways does Intel foster that innovation and that community engagement for developers?

Paula Ramos: That is a great question, because we have been working so hard on that part as well. So we have multiple ways. We are creating multiple ways to create this innovation with developers. We have one program, that is the Innovator Program, where we have multiple developers around the globe that they can try, they can test technology. They can make their own applications, and they can share that with us. So just stay tuned, for example, in my LinkedIn or in my network as well: we are highlighting some of these innovators. This is one thing that we have. And basically they create their own repository. They fork their repository, and they create new applications or improve the application with the contribution.

So another thing that we have is Google Summer of Code. We have a program with Google every year where we have multiple proposals, and we have several developers around the globe as well working with us for three months with different mentors in the OpenVINO team. And, for example, you mentioned about CVPR.

So, we worked with Anomalib. There is a library that also we have in the OpenVINO ecosystem, and we have two proposals last year about Anomalib, and one of these proposals the student that was involved in Google Summer of Code and the mentors and the professor as well, they created a paper. The paper was submitted in the workshop of anomaly detection, Visual Anomaly Inspection Workshop at CVPR, and that was accepted. So we are closing also the gap in between industry and the academia with conferences. We are also participating with the students and developers in those conferences through programs as, for example, Google Summer of Code.

But more than that, for sure we are moving so fast also in relations with universities: what kind of things we can work with universities, helping them to create some research and research proposals that Intel also can support.

At CVPR we are sponsoring as well the challenge in this workshop about anomaly detection. We try also to invite developers, and we create a marketing campaign around the challenge to invite developers to participate in that challenge. We received more than 400 participants and more than 100 submissions. That was an amazing and remarkable number around maybe one month and a half that we received, and we can see how the knowledge is moving in using anomaly detection.

For sure, talking about OpenVINO we have multiple things. As I mentioned before, OpenVINO is an open source tool, and we have a repository where we have different kinds of contributions depending on the product. So we have OpenVINO, OpenVINO notebooks. We have OpenVINO build and deploy. In that repository, OpenVINO build and deploy, you can find all the Edge Reference Kits that we have been talking about today. OpenVINO notebooks, you can find the tutorial; and in the OpenVINO repository you can find the API.

So we have a huge ecosystem where we are trying to touch not just the inference part, also the training part with anomaly detection, Anomalib, and also OpenVINO Training Extension. So we have a huge ecosystem that I really want to invite all the developers and all the people that are watching this podcast or listening this to this podcast to visit those repositories, visit the organization, “openvinotoolkit” in GitHub, and you can find all the repositories that I’m talking about.

Christina Cardoza: Absolutely. It’s exciting hearing all of these different resources, all these different ways developers can get started. I’m excited to see, moving forward, what types of solutions and innovations developers continue to build, and I hope they take you guys up on some of these events and meet you—whether that’s in person or virtually. I know sometimes it can be intimidating when you’re getting started in these areas, but having companies like Voxel and Intel support developers, that’s great to see.

And I also saw, Jason, in addition to the virtual office hours, there’s availability to do one-on-one meetings. So if developers feel intimidated somehow or don’t want to ask a question in a group setting, it’s great that you guys are making yourself available to help developers when and where they need it.

So I want to thank you both again for joining us on this podcast. Before we go, if there’s any final thoughts or key takeaways you want to leave developers with as they go on this journey, engage with each other, and engage with yourselves. Jason, I’ll start with you.

Jason Corso: Great. Yeah, thanks very much. So, I mean, first parting thought would be that I think I just want to express my thanks to the developer community that we’ve built over the last four or five years. We wouldn’t be where we are today without the community. It’s such a vibrant and rich environment.

But the second thing is that actually we’re hiring; we’re hiring developers. I mean, actually across the board we are as we grow, after we closed our Series B earlier this year. But for this conversation, machine learning engineer roles, both for core engineering work as well as developer relations work. We believe in developers so much, so we hire individuals that are fully trained and can write papers, can write code, and so on, but their role is actually building bridges with the community.

And then maybe just the last parting remark is that we, as a company we are open source driven, but we do actually have dozens of customers that use our commercial enterprise version that we call FiftyOne Teams. It kind of relaxes that individual user local data work and allows you to develop the same functionality together in teams—in the cloud or on-prem. And we’d love to engage in conversations around FiftyOne Teams as well with your community. We have customers, many of which are in the Fortune 500, but across manufacturing, security, automotive—a pretty broad-base customer base. So, thanks.

Christina Cardoza: Yeah, absolutely love to hear about job openings. It shows this space is growing, this space is becoming important, and some of the innovations and transformations that we talk about on “insight.tech” wouldn’t be possible without developers. So, exciting opportunity for anybody listening to go join the Voxel51 team.

Paula, always love having you on the podcast. Thank you, again. I feel like every conversation there’s something new to talk about, something new happening in the AI space. So, curious what our next conversation will be about. But before we go, are there any final thoughts or key takeaways you want to leave with us?

Paula Ramos: Yes, for sure. So, first of all, thank you. Thank you, Christina, for creating this space to talk about what we have. And thank you also to Voxel51. We have been creating a great relation with Voxel51—different conferences, we try to share some space together.

And this also talks pretty well about that we have the real intention to work in the open source community. So we are open to work with all of you: try to find the best path to developers, because here the most important thing is developers. So, the company for sure is really important. We have a lot of things to learn from the company: what kind of products we can provide, what kind of tools we can provide to developers. And always we are thinking that we need to enable you to use this hardware in software that we can provide and you can accelerate; you can improve your pipelines and your workloads. That is the main intention.

We have right now a lot of things to share with you. So we talk about OpenVINO, Edge Reference Kits, but more things are coming in the future. For example, we have the new AI PC that you can try. We have a new engine in the microprocessor—that is the NPU, Neural Processing Unit—that we can also expedite and accelerate part of the conventional and generative AI, conventional AI, generative AI, process with that small device. This is one of the things that we can talk about in the future, Christina, for sure. Thank you again, and I’m looking forward to connecting with all of you.

Christina Cardoza: Absolutely, and you talked about earlier how some of these innovations or these tools you have available are making it easy for developers to start working no matter what hardware they’re using, and the AI PC just makes it that much easier for the AI development, deployment, performance of your solutions, all that great stuff. So I know Intel has a lot of resources around AI PCs that we’ll make sure to provide to developers as well.

But thank you both again for joining us today. Thank you to Intel and Voxel51 for these great resources and communities you’ve created for developers and spaces for them to get started and get that support. Until next time, this has been “insight.tech Talk.”

The preceding transcript is provided to ensure accessibility and is intended to accurately capture an informal conversation. The transcript may contain improper uses of trademarked terms and as such should not be used for any other purposes. For more information, please see the Intel^® trademark information.

This transcript was edited by Erin Noble, copy editor.

Christina Cardoza: Hello and welcome to “insight.tech Talk,” where we explore the latest IoT, AI, edge, and network technology trends and innovations. I’m your host, Christina Cardoza, Editorial Director of insight.tech, and today we’re taking on the conversation of the network edge with CK from CASwell. But before we get started, let’s get to know our guest. CK, what can you tell us about yourself and what you do at CASwell?

CK Chou: Hi, Christina; hi, everyone. My name is CK, with over 10 years of experience in CASwell for product management. My main focus has been on serving customers in Europe and the Middle East. Over the years my mission is to build strong relationships with clients across these regions, ensuring that the solutions from CASwell meet their specific needs and standards.

And about CASwell—originally began as a division dedicated to network-security applications. Over time our expertise and focus grew, leading us to branch out and establish ourselves as a standalone company in 2007. Over the years CASwell has placed a strong emphasis on R&D to stay at the forefront of technology and innovation. However, we were not satisfied as only a player for networking, so expanded our business into information and operation applications. I should say that our journey from a small division to an independent company wasn’t just about getting bigger; it was about getting better at what we do.

Nowadays, CASwell is a leading hardware solution provider for IT and OT industry in Taiwan, specializing in design, engineering, and manufacturing of not only networking appliance but also industrial equipment, edge computing device, and advanced edge-AI solutions which can meet the demand for the current, modern applications.

Christina Cardoza: Great, and I’m looking forward to digging into some of that hardware. But before we jump into that, I want to start the conversation trying to understand a little bit more of why companies are moving to the network edge. I like how you said in your introduction: you’re trying to stay at the forefront of technology and innovation and get better at what you do. And I think a lot of businesses are trying to do the same, and they look to CASwell to help them along that journey. But why are they moving to the network edge today, and what challenges are they facing on their journey?

CK Chou: If we are talking about the edge computing, we all know that it is all about handling data right where it is created instead of sending everything to the central server. This means faster response and less internal traffic, which means it is perfect for things that need instant reactions like manufacturing, retail, transportation, financial services, and etcetera.

Let me say it in this way. Imagine you are in a self-driving car and something unexpected happens on the road. You need your car to react instantly because every millisecond counts, okay? You cannot afford a delay waiting for data to travel to a distant server and back. It’s not like waiting for a loading sandbox when you’re using your computer, right? In self-driving scenarios any delays could mean life or death. This is just an example where edge computing comes in, handling data right at the source to make those split-second decisions.

And of course it’s not just about the speed; it’s also about keeping your information safe. If sensitive data like your financial information can be processed locally instead of being sent over the internet to the central server, there’s a lower chance of it being intercepted or hacked. The less your data travels around, the safer it stays.

This kind of localized processing is also super important in other areas like health care—which needs instant diagnostic results—machines in a factory detecting problems. By processing data on the spot, edge computing help keep everything running smoothly, even in places where internet connections might be unreliable. So, in short, edge computing is all about speed, security, and reliability. It brings the power of data processing closer to where it’s needed most—whether it’s in your car or your doctor’s office or on the factory floor.

But from what I hear from some of our customers, moving to the network edge is not always easy. It’s a big step and comes with its own set of challenges. Companies face things like increased complexity in managing systems, higher infrastructure cost, limited processing power, data-management issues, and more. Despite these challenges, the benefits of edge computing are too significant to ignore. It can really boost the infrastructure performance, improve security, and save the overall cost, and eventually making it worth the effort to overcome all those hurdles.

Christina Cardoza: Yeah, absolutely. I can definitely see the need for network edge and edge computing with all the demands of the real-time data processing, like you mentioned—the enhanced security, improving user experiences.

But I feel like a lot of times when we discuss the edge it feels very abstract. We know all of the benefits and why we should be moving there, but how do we move there? Is there a network-edge device, for instance, that is able to help us move to the edge and get all of these benefits? What does that look like?

CK Chou: Challenges that I mentioned earlier make moving to the edge seem expensive and complicated, but if companies can have reliable edge devices integrated, it can provide innovative, dependable, and affordable hardware features to help the companies to overcome these challenges so they can allocate their limited resources and focus more on building and managing their infrastructure, maintaining their data, and improving the security, or training their staff.

That’s why companies need to work closely with the edge-device provider, like CASwell. Our customers can always count on us because we design the right equipment for the right use case and ensure the edge devices are the key for their edge journey and make their transition to the edge smoother and easier. So, at the end of the day, having the right device with the right features are essential, but it’s only with the right partner—like CASwell. We support them from the hardware perspective, allowing companies to focus more on their specialization. Each party plays its own role, enabling companies to truly do more with this in their edge journey.

Christina Cardoza: I know you mentioned obviously it’s important to have the right features and reliable, affordable hardware, and that helps you build and manage infrastructure and maintain that data that’s really important. But can you talk a little bit more about what those features and hardware capabilities look like? When companies are looking for a network-edge device, what type of capabilities are really going to bring them success?

CK Chou: Okay, it is a tricky question for me. If I’m talking about my dream edge device, it needs to be small and compact, also packed with multiple connection options like SNA, Wi-Fi, and 5G for different applications. And it would also be nice to have a rack design that can operate in a harsh environment and handle the right range of temperature if users want to install the equipment in stony cold mountains or hot deserts. Also, offer powerful processing but consume low power. And, of course, the most important thing is the cost for this all-in-one box needs to be extremely low.

Getting all that in one device sounds perfect, right? But do you really think that would even be possible? Okay, I can tell you the truth is, companies at the edge don’t really need an all-in-one box. What they really need is a device with the right features for their specific environment and application, and that’s what CASwell is all about.

We have a product line which can provide a variety of choices, from the basic models to high-end solutions and from IT to OT applications. Whether it’s for a small office, a factory, or a remote location, we have got options designed for different conditions and requirements. So, with the right partner, companies can easily find the right edge device without paying for features they don’t really need.

Moving to the edge computing certainly costs a lot, so we need to do it smart and efficient. The idea is to ensure that every edge player can get exactly what they need to optimize their operations and stay ahead of this game. So, sorry that there’s no certain answer for your question here. In my opinion, if an edge device can offer the right features, right capabilities with an affordable cost for the specific use case, then it’s just a good edge device that we are looking for.

Christina Cardoza: Yeah, absolutely. No, I love that businesses or companies, they don’t necessarily need an all-in-one box. I think so many times the businesses are focused on finding something that is cost effective that tries to meet all their needs, and they sort of lose sight of what their needs actually are and how a device can help them and the benefits in the long run. So, that’s definitely great, and I want to get into how partnerships work with CASwell, as well as the different product lines that you do have a little bit deeper.

But before we get there I’m a little curious, because obviously when we talk about edge today, AI is so closely related to it. AI at the edge is a term that’s going around these days, and so I’m curious what the role here is at the network edge, especially when we’re talking about network-edge devices.

CK Chou: We know that nowadays AI-model training is done in the cloud due to its need for massive amounts of data and high computational power. If you do a quick search online, you’ll find lots of pictures showing how an AI factory or AI data center need to be. Imagine something the size of a football field and filled with dozens of big blocks, and each block is packed with hundreds of servers, all linked together working nonstop on model training.

I agree that such an AI server sounds amazing, but this is too far from our general use case and not is able to be afforded by our customers. As we talked about earlier, the concept of edge computing is all about handling data right where it is created instead of sending everything to a central server. So, if we want to use AI to enhance our edge solutions, we cannot just move the entire AI factory to our server room, unless you are super rich and your server room is the size of a football field.

Instead, we keep the heavy-duty, deep learning tasks in a centralized AI center and shift the inference part to the edge. This approach requires much less power and data, making it perfect for edge equipment. We’re already seeing this trend with AI integrated into our everyday devices, like mobile phones and AI-enabled PCs. These device use cloud-trained models to make smart decisions and provide our personalized experiences and enhance user interaction.

Building on this trend, edge-AI servers are coming into the picture of CASwell by integrating with the general computability; we often use a GPU engine here. This edge server can handle the basic AI calculation on top of our existing hardware. This means faster decision-making and the ability to use AI-driven insights in real time, whether it’s for cybersecurity, small factories, or other edge applications.

CASwell is now building a new product line for edge-AI servers designed to bring AI capabilities right from the data center to the edge, giving us the power of AI instantly, and it puts AI directly in the hands of those who need it and right when they need it.

Christina Cardoza: So, tell me a little bit more about that product line or the other products that CASwell offers. You mentioned that you have a whole suite of tools to help businesses depending on what their needs are, their demands, and what they’re trying to get. So, how is CASwell helping these businesses address their network-edge challenges and demands?

CK Chou: I can introduce a model, CAF-0121. The CAF-0121 is an interesting entry-level desktop product from CASwell, built around Intel’s new generation Atom^® processor, which offers a great balance of performance and power efficiency. This small box also can provide 2.5 gig support to fulfill the basic infrastructure connectivity, plus its compact and fanless passive-cooling design, which is suitable for edge computing applications.

But we can see a trend where the edge environments are becoming more challenging than we initially expected. End users want to install edge equipment not just in the office space with air conditioning or on clean, organized racks, but also in OT environments like a warehouse, factory floors, and even cabinets without proper airflow. The line between IT and OT is becoming more broad, and more users are looking for solutions that can work in both IT and light OT environments.

As a compromise, CASwell decided to develop this CAF-0121 that can handle a wider temperature range from the typical 0º–40º up to something like -20º–60º. Our goal with this new model is to provide OT-grade specs at an IT-friendly price. This means users can cut down on the resources needed to manage their infrastructure and make deployment much simpler. They can use the same equipment across both IT and OT applications, making it easier to standardize and maintain their technology setup. So the approach for CAF-0121 allows business to adapt to different environments without needing separate solutions for each scenario, which is really an exciting product.

Christina Cardoza: Yeah, that’s great that you developed the CAF-0121 to help businesses in all of their needs. It occurs to me as we’re talking about this, the different temperature ranges that they need to meet, the cost ranges, that not only are businesses having challenges, but sometimes it can be challenging for partners like CASwell to create these solutions that meet their demand.

So, I’m just curious if there’s any insight that you can provide when developing this product, if you guys had any challenges to meet all of these demands and how you were able to overcome them?

CK Chou: The technology around the thermoelectric module—we call it TEM—is the one we are relying on for CAF-0121. TEM is already a proven solution for cooling overheating components. It is common in things like medical devices, car systems, refrigerators, water coolers, and other equipment that needs quick and accurate temperature control.

These slim devices work on creating a temperature difference when electric current passes through them, causing one side to heat up and the other side cool down. The more current we send through, the bigger the temperature difference we get between the two sides. And of course TEM does not run on its own. It is controlled by a microcontroller and the thermal sensor that monitors the temperature inside the device. The firmware that we have programmed into the microcontroller takes those temperature readings and decides when to turn the TEM on and how much current we should send through.

We have gone through countless trials and adjustment with the firmware settings to ensure our equipment stays in the ideal temperature range. And we also had to watch out about the condensation reaction, because if a TEM cools down too quickly, it can cause moisture to form on the module surface. And if the moisture gets onto the circuit board, it could cause serious damage. So an appropriate liquid isolation solution between moisture and a circuit board is also necessary.

While people are normally using the cooling capability of the TEM, we had a different idea of why not leverage both the cooling and heating capability to help our edge device to operate in a wider temperature range? So the overall concept is that by leveraging the heating capability of the TEM, we can indirectly expand the operation temperature range of the system to a lower degree. And, conversely, by using the cooling capability it can cool down the system when the internal ambient temperature rises to a certain high level.

Let me say it in a simple way. When the room is getting cold, TEM operates as a heater; and when a room is getting hot, TEM operates as a cooler. With a TEM, we are no longer limited to the operation temperature range of the individual components we have selected. It helps us bridge the gap, allows us to expand the temperature range of our equipment beyond what the components could typically allow. This means we can push the temperature boundaries by using the TEM and the device can still maintain reliability.

And some people might think, why don’t we just use industrial-grade components that support a wider temperature range and make our life easier? Reality is those wide-temp components can sometimes cost twice as much as standard commercial ones, plus the general chassis designed for this case is usually large and heavy. And then of course the most important reason is if we build our equipment just like everyone else, why would customers choose us over the competition? If that is the case, CAF-0121 would just end up being another costly device with bulky thermal fans designed to support wide temperature ranges, and this is not what we want.

That’s why we have put a lot of effort into studying the characteristics of the TEM more closely and focusing on selecting the right thermal-conductivity materials, fine-tuning our firmware settings, and testing our device in temperature-control chambers day and night. Our goal is to redefine what edge computing hardware can be by offering solutions that are adaptable to various temperature environments, compact and lightweight, and also still being competitively priced.

Christina Cardoza: Yeah, it’s amazing to hear those different wide ranges of temperature environments you were mentioning in cars and refrigerators, so I can see the importance of making sure that it’s consistently reliable and it provides that performance.

So, do you have any customers that have actually been using CAF-0121 and anything you can share with how they’re using it or in what type of solutions it is in?

CK Chou: This box is going to mass production in October this year, which is the next month, and we have already got a few thousand purchase orders from a major European customer focused on cybersecurity applications and planning to use this device in small office, warehouse, and possibly outdoor cabinets for electric-vehicle charging stations that need wider temperature support. This really highlights the advantage of CAF-0121. The customer can use it across both IT and OT applications without needing separate solutions for different operation temperature conditions, and of course saving customers from having to spend extra money.

We also sent samples to around seven to eight potential customers across various industries here, including cybersecurity, SD-WAN, manufacturing, and telecom companies for instant traffic management. The feedback has been fantastic. Everyone loves the competitive price, which makes our device a great deal. And also the compact size is another big win, because it can fit into tight spaces and helps lower our shipping cost. Also, reduces the carbon footprint.

You know, in today’s market, pricing is a huge factor. We need to do cost-effective solutions but cannot compromise on performance and flexibility. So it’s clear that our approach is hitting the mark for customers who need the reliable and scalable edge solutions that don’t break their bank. The excitement we are seeing from these industries really proves that we are on the right track, and CAF-0121 is exactly the kind of solution that can make their needs.

Christina Cardoza: I can definitely see why the solution needs to be smart and compact, but then also fast and reliable, high performance. So, I’m curious how you actually make that happen. And I should mention “insight.tech Talk” and insight.tech as a whole, we are sponsored by Intel, but I know Intel has a lot of processors that make these devices possible, that make them be able to run fast in these different environments and in these small form factors. So, I want to hear a little bit more about how you work with technology partners like Intel in making your product line possible.

CK Chou: As we discussed earlier, a solid edge computing device should have just the right processing power packed in a compact size, a variety of connection options, energy efficient, and of course a competitive price. These are really the basic must-haves for any edge computing device.

That’s why we have chosen the Intel Atom processor for this project. With the Atom we can provide the right level of performance and still keep power consumption low. And also thanks to Intel LAN controller that helps us easily add the support for 2.5 gig Ethernet to this box to ensure the capability with most infrastructure requirements and more. The Atom has built-in instructions that can accelerate IPsec traffic, making it an excellent choice for security-focused applications. So, whether you are dealing with data encryption, secure communications, or other security jobs, this processor is up to the challenge.

And if we wanted to further enhance the security, Atom is also integrated with BIOS Guard and Boot Guard to provide a hardware root of trust. With these two guards we are not just talking about great performance and efficiency, we are delivering a high level of protection for the BIOS and the boot-up process. This level of security is crucial, especially for edge devices that need to handle sensitive information and critical tasks without compromising protection.

I can say that only Intel offers a one-stop shop for all these features among the various players in this market. They don’t just provide the hardware, but also the driver and firmware support. This level of integration has made the development of the CAF-0121 project so much easier, and it has really shortened our time-to-market. When you have got the processing power, security features, and even software support all coming from one reliable partner, Intel, it certainly streamlines the whole process. This not just simplifies the engineering and development work but also ensures everything works seamlessly together.

So, with Intel’s comprehensive support, the hardware designer—like CASwell—can focus more on optimizing performance and less on troubleshooting capability issues. This is a big win for both us and our customers, allowing us to deliver high-quality, reliable edge computing solutions faster and efficiently.

Christina Cardoza: Absolutely; that’s great to hear. And I’m sure—we kept talking about in this conversation making things more cost effective, more affordable, so I’m sure being able to leverage the technology expertise or the technology processor and other elements from a partner like Intel, that helps you be able to focus on your sweet spot and not have to build things from scratch and make things more expensive than they need to be. So, great to hear how you’re using all of that different technology.

It’s been a great conversation. You’ve really been able to take a technical topic and make it more digestible and understandable. Unfortunately, we are running out of time, but before we go I just want to throw it back to you one last time, if you have any final thoughts or key takeaways you want to leave our listeners with today.

CK Chou: I started working at CASwell 10 years ago, and things were pretty different back then. At that time most of the processing power was centralized. Companies were all about making their server super powerful, giving them the fast internet connections for gathering all the data from the edge. Servers were packed with multiple features to handle every use case you could imagine.

Times have changed. It’s all about instant processing and real-time AI calculations. Businesses need to make quick decisions right at the source of the data instead of sending everything back to the central server. That’s why edge computing has become such a big deal. It lets companies process data on the spot without any delay.

But when all the network players are shifting toward edge solutions, the real challenge is how do we make our equipment different and better than everyone else? So this project, CAF-0121, we have gained some really valuable know-how using an old-school technology as an innovative thermal solution for edge equipment and tried to bring added volume to our products in this highly competitive market. We also want this small success to inspire our R&D team to stay creative and think outside the box, and not just stick to the traditional way of doing things.

Also, thanks to the support from Intel about their edge solutions, including edge-optimized processors—which build in deep learning–inference capabilities—various LAN options for different connectivity needs; and of course including all the documents for integration, drivers, and firmware support. This collaboration has really helped us push our designs to the next level.

Finally, our goal is very simple: aiming to set a new standard of edge computing equipment and providing flexible edge solutions to help customers tackle challenges from the cloud and through the network and all the way to the intelligent edge.

Christina Cardoza: Well, I can’t wait to see what else CASwell does in this space—as well as the CAF-0121 when that comes—different market solutions that companies are going to be leveraging this for. I invite all of our listeners to visit the CASwell website, contact them, see how they can help you in all of your edge and network-edge needs. As well as visit insight.tech as we continue to cover partners like CASwell and how they’re innovating in this space.

So, I want to thank you again for joining us today, CK, as well as our listeners for tuning in. Until next time, this has been “insight.tech Talk.”

The preceding transcript is provided to ensure accessibility and is intended to accurately capture an informal conversation. The transcript may contain improper uses of trademarked terms and as such should not be used for any other purposes. For more information, please see the Intel^® trademark information.

This transcript was edited by Erin Noble, copy editor.