Unleashing Intelligence at the Edge
Mousumi Karmakar · 2025
The widespread adoption of intelligent edge computing has led to a crucial technological frontier: the seamless integration of machine learning algorithms into embedded systems. Edge AI brings intelligence directly to embedded devices by running AI models locally. This reduces reliance on the cloud, enabling faster decisions, improved privacy, and reliable operation even without constant internet connectivity. This chapter explores the incorporation of machine learning algorithms into embedded systems, focusing on challenges, possibilities, and future prospects. This chapter starts with an introduction to embedded systems and their limitations. It then delves into the fundamentals of machine learning algorithms that are suitable for these resource-constrained platforms. This chapter discusses methods for optimizing algorithms to reduce energy usage and fulfil real-time requirements. It also examines strategies for hardware acceleration. Real-world case studies spanning various fields, such as IoT, automotive, healthcare, and consumer electronics, demonstrate practical applications of machine learning in embedded systems. This chapter offers insights for researchers, practitioners, and policymakers and concludes with recommendations for further research.