Overview

Embedded AI for Resource-Constrained Systems provides a comprehensive roadmap for engineers, computer scientists, and IoT specialists seeking to bring machine learning (ML) intelligence to devices with limited power, memory, and computational resources. The book begins by framing the paradigm shift from cloud-centric AI to on-device intelligence, emphasizing the unique challenges and opportunities of deploying ML models on embedded hardware such as microcontrollers and edge processors. The early chapters introduce the fundamentals of embedded ML, including the hardware architectures that underpin resource-constrained systems. Readers learn about the trade-offs between model complexity, accuracy, latency, and energy consumption, and how these factors influence the design and deployment of ML solutions at the edge. The book systematically explores model compression techniques-such as pruning, quantization, and knowledge distillation-that are essential for fitting sophisticated models into small memory footprints and achieving real-time inference. Subsequent chapters delve into optimizing inference latency, power-aware system design, and benchmarking performance. The text covers practical tools and frameworks, including TensorFlow Lite for Microcontrollers and CMSIS-NN, and provides hands-on guidance for converting, quantizing, and deploying models on real hardware. Advanced topics include federated learning, on-device training, and sensor fusion, highlighting how embedded systems can adapt and learn from local data while preserving privacy. A capstone project walks readers through the end-to-end process of deploying a vision model on a microcontroller, reinforcing key concepts with practical implementation details. The book concludes by surveying emerging trends such as neuromorphic computing, spiking neural networks, and the evolving ecosystem of TinyML hardware accelerators. Overall, this book equips practitioners with the knowledge and tools to design, optimize, and deploy efficient, intelligent embedded systems, bridging the gap between theoretical ML advancements and the practical realities of edge computing.

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