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

The rapid rise of AI applications is making a transformational impact on society, reshaping our business and personal lives. In addition, the ubiquitous availability of mobile devices now able to deploy Large Language Models (LLMs) to solve real-world problems represents the next exciting evolutionary stage in the development of AI applications. This course will answer three fundamental questions related to the rise of mobile AI: 1. What are the technological trends driving AI to the edge? 2. What are the tools and techniques required to deploy LMs on Arm-powered mobile devices? 3. Why is Arm the best-placed platform to harness the advantages of LMs on the edge? We’ll start your learning journey by taking you through the basics of AI at the edge and deploying LMs on Arm-based mobile devices. We’ll then introduce you to the tools needed to make this happen, with a focus on how to optimise LMs on mobile devices using model compression techniques such as pruning and quantisation. Along the way, our practical lab exercises will show you how to implement LMs on a mobile device, and all you’ll need is an Arm-powered phone to get started. We’ll also feature key engineers from Arm discussing current and future developments in AI at the edge, providing you with industry relevant insights into the evolution of this exciting new technology. The course will target early and mid-career professionals looking to upskill or supplement their CVs, or undergraduates with programming experience. AI at the edge is powered by Arm. Discover the tools and techniques you’ll need to deploy LMs on ubiquitous Arm-based mobile phones by enrolling in this course today! 3b:T40b,A

• An understanding of technological trends driving AI to the edge, especially on mobile devices.
• An understanding of key concepts in mobile AI: on-device processing, power efficiency and real-time inference.
• How to train language models with domain-specific data.
• An understanding of the fundamentals of edge AI model deployment including quantisation and model compression techniques.
• How to develop applications using LLMs.
• An appreciation of security and privacy issues related to AI at the edge.
• Insights into future trends and innovations in mobile AI.

• Experience in programming and basic understanding of machine learning and AI.