Building an AI Agent on Arm: Returning to Cambridge as a Guest Lecturer
About a month ago, I had the privilege of returning to the University of Cambridge—not as a student this time, but as a guest lecturer. I was invited to speak at an Engineering Applications lecture, where I demonstrated an AI agent running on an Arm-based CPU. The experience was especially meaningful as it took place in the very same lecture theatre where I once sat as an engineering student.
Demonstrating AI on Arm
The core of my lecture focused on showcasing how to build and deploy an AI agent on Arm-based hardware. As AI continues to evolve, so does the importance of running machine learning models efficiently on edge devices. Arm’s architecture, with its energy efficiency and widespread deployment, presents a compelling platform for AI applications.
During the session, I walked through the practical aspects of setting up an AI agent, from selecting the right tools and frameworks to optimizing performance on Arm CPUs. My goal was to give students a tangible sense of how theoretical concepts translate into real-world engineering challenges and solutions.
Creating the Learning Path Tutorial
Inspired by the positive engagement during the lecture, I developed a tutorial titled “How to Build an AI Agent on Arm”. This tutorial is now part of the Arm Learning Path, a growing library of step-by-step guides designed to help developers and students get hands-on experience with emerging technologies.
The tutorial covers:
- Setting up the development environment
- Choosing a lightweight ML model
- Deploying and testing on Arm-based devices
- Best practices for performance and efficiency
Whether you’re a student or a developer looking to expand your AI skills, the tutorial offers a clear roadmap for building intelligent applications on Arm.
Reflections
Standing on stage at Cambridge was a full-circle moment that reminded me how far I’ve come—and how important it is to give back. I hope the lecture not only provided valuable technical insights but also inspired attendees to explore the intersection of AI and embedded systems.
If you’re curious about what it takes to build AI on Arm, I encourage you to explore the Arm Learning Path. It’s a great starting point for anyone looking to bring intelligent applications to edge computing platforms.
