AI at the Edge on Arm: Deploying LLMs for Mobile Devices

About this course

By studying this course, you will gain an understanding of the technological trends driving AI to the edge, key concepts in mobile AI including on-device processing and real-time inference, and how to train language models with domain-specific data. You will also develop practical knowledge of edge AI deployment fundamentals, including quantisation and model compression, build applications using large language models (LLMs), and gain an appreciation of the security, privacy, and ethical challenges involved. This course explores three fundamental questions shaping the future of mobile AI: what is driving AI to the edge, what tools and techniques are needed to deploy LLMs on Arm-powered mobile devices, and why Arm is uniquely positioned to harness the advantages of edge AI. Across six modules, you will move from foundational concepts in training and inference through to advanced optimisation techniques and security considerations, equipping you with the knowledge and skills to deploy and develop AI applications at the edge. The course is taught by Michele Magno, Head of the Center for Project-Based Learning at ETH Zürich and a leading researcher in Tiny Machine Learning and energy-efficient IoT, alongside Pietro Bonazzi, a PhD Candidate in Efficient AI Computing at ETH Zürich specialising in memory-efficient, low-latency systems for edge platforms. A basic understanding of programming and machine learning is recommended.

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