Executive Viewpoint: Jason Kridner, BeagleBoard.org

April 02, 2024

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With a new offering hitting the shelves, I thought it was a good time to catch up with Jason Kridner, the Co-Founder and Board President of BeagleBoard.org.

Rich Nass: Tell me a little bit of the history of BeagleBoard and describe the current offerings.

Jason Kridner: BeagleBoard.org has a long history. The organization started building small, low-power single-board computers back in 2007. Our passion since the beginning has been to serve the open-source developer ecosystem, enabling education and better tools for building embedded systems. We do this with our open hardware designs and community-based support—and the results have been amazing. We’ve shifted the way people develop embedded systems, clearly for the better.

BeagleBoard.org has established itself as a trusted and reliable provider of SBCs. By prioritizing open-source principles and supporting both educational and industrial applications, BeagleBoard.org has played a pivotal role in democratizing access to technology and empowering individuals to innovate and create. Our dedication to these principles for over 15 years is a testament to the enduring impact and importance of open-source initiatives in driving technological advancement and fostering inclusive learning environments.

We have produced over 10 million boards, with consistent availability of products like BeagleBone Black, which debuted in 2013. We continue to make BeagleBone Black and an industrial temperature variant available, as well as several other designs, and we’ve introduced four new designs over the past year, including our first microcontroller board, BeagleConnect Freedom, featuring 1-Km-capable wireless communications running Zephyr and supported with Micropython. Another offering, BeaglePlay, is a flexible user interface plus gateway design supporting the same 1-Km-wireless protocol and single-pair Ethernet, along with some innovative features to reduce the work required to add a huge body of sensors without complex wiring. We’ve also introduced a pair of interesting RISC-V-based development options for those looking to explore that emerging ISA.

That said, we are really excited to talk about our latest offering, BeagleY-AI, which features the ability to work with a host of existing enclosures and add-on hardware over an emerging industry-standard 40-pin header. BeagleY-AI stands out with its open-source hardware design, fanless operation, and 4 TOPS capable deep learning engine for AI workloads.

Nass: How does the BeagleY-AI board differ from what’s already available in the industry? I understand it employs the latest processor available from Texas Instruments. Was TI involved in the development of the board? And in what other ways was the company involved? And why is this important or beneficial to a developer?

Kridner: The long-standing partnership between BeagleBoard.org and Texas Instruments has been instrumental in our success. The commitment to utilizing TI processors in BeagleBoard products, including the AM67X that’s in the BeagleY-AI board, underscores a shared vision for advancing technology and providing cutting-edge development platforms to makers and industry partners. The AM67X processor brings significant benefits, such as energy-efficient machine-learning capabilities, low-latency cores for timing-critical applications, and support for standard high-speed I/O interfaces like USB 3 and PCIe Gen 3. I believe that the low power, accelerated vision processing, and production stability gained from using the AM67S SoC helps BeagleY-AI stand out from its peers.

The collaboration between BeagleBoard.org and Texas Instruments extends beyond hardware integration, with TI's hardware and software design teams actively involved in the BeagleY-AI testing and review process. Their A supply chain and ongoing technological advancements underscore their commitment to supporting the open-source community, ensuring the success of initiatives like BeagleBoard.org.

Nass: The new board has AI in its name, so tell me what that means.

Kridner: The short answer is that it means that we have a built-in accelerator with the ability to execute a deep learning model at very high rates, similar to those on much bigger and more power-hungry systems.

The longer answer is that it means we are focused on providing developers with a better tool for understanding what is possible in AI, including things like object detection, pose estimation, and image segmentation. We do this through a body of easy-to-access examples and materials on our documentation site, docs.beagleboard.org.

Nass: In terms of real-world examples, what are some of the interesting places that Beagle is being deployed?

Kridner: It’s incredible to see the breadth of applications where Beagles have been utilized, ranging from healthcare solutions like affordable, open-source real-time PCR machines used in COVID-19 detection to underwater rescue drones, AI-powered machines, and even space missions. This diversity highlights the robustness and adaptability of Beagles, making them a go-to choice for innovators across different industries seeking reliable and versatile open-source hardware solutions.

But we only hear about a small percentage of where Beagles are used because it’s up to the developers to decide what they want to share with us. We suspect that there are thousands of applications out there, and we are here to support them when needed. One that we are aware of is a laser-engraving application.

Nass: BeagleY-AI is built with “open-source hardware.” What does that mean and why is it significant? Is open source something to fear? Is it secure and safe?

Kridner: Quoting the definition, “open-source hardware” is the hardware whose design is made publicly available so that anyone can study, modify, distribute, make, and sell the design or hardware based on that design. This really gets to the heart of who BeagleBoard.org is and to fully answer, I think we need to start with a look back in history.

The history of open-source hardware closely parallels the history of open-source software. When computers were first introduced in the 1950s and 1960s, providing the software source along with hardware design information was standard practice, critical for users to understand how to program these complex machines and even repair them when something goes wrong. This is a level of understanding that is denied by keeping a design closed.

When building a safe and secure system, high-quality reviews are critical. This is why mission-critical systems for the New York Stock Exchange can run Linux. A broad group of experts are enabled to chime in on possible vulnerabilities. Developers have the option to choose when, where, and how to lock things down to meet their own goals, rather than the security goals of someone else. Lots of eyes are looking at that body of code to make sure it’s robust. Personally, I feel far more secure running software with that degree of scrutiny than anything produced by any single company, and it is the same thing for hardware.

Open-source hardware means you can choose to secure things where you best see fit. It means you can even build the boards yourself if that is what you need to meet your security goals.

Nass: What are the programming languages and environments you’d expect a developer to employ when working with the new BeagleY-AI, and why?

Kridner: All of them! The nice thing about building on Linux is that most languages are supported. We’ve moved away from trying to build language-specific bindings to control the hardware and have instead focused on the interfaces provided by Linux and Zephyr.

With Zephyr, we can build something as small as 4 kbytes and that’s usually small enough for just about any system or subsystem—certainly in the prototype stage. It lets you focus on your code and just pull in supporting code as you want it to save time in your development. You can go as far as getting a full POSIX environment, so building runtimes like Micropython is pretty easy to achieve.

So, Python, JavaScript, C, C++, Go, and Rust get a lot of the focus. We enable a self-hosted Visual Studio Code environment, but we leave it to others to provide value-added libraries and make sure Linux and Zephyr provide the interfaces needed by developers.

For the deep learning algorithms, there’s a fair bit of higher-level language integration and that’s primarily focused on Python and C++ with familiar APIs, like TensorFlow Lite. This isn’t an area where operating systems have integrated the interfaces yet, but I expect that to change.

Nass: How can developers become part of the Beagle community?

Kridner: BeagleBoard stays closely aligned with the needs and feedback of its users and developers, ultimately leading to more relevant and impactful product designs. We encourage joining the BeagleBoard.org community through our forum at forum.beagleboard.org.