About this book:

*Edge AI Engineering* provides a comprehensive technical roadmap for deploying machine learning models on resource-constrained hardware such as microcontrollers, DSPs, and NPUs. The book centers on the fundamental engineering trade-off between accuracy, latency, and energy consumption. It details essential model compression techniques, including post-training and quantization-aware training, structured and unstructured pruning, low-rank factorization, and knowledge distillation. By exploring efficient architectures like MobileNet and automated methods like Neural Architecture Search (NAS), the text demonstrates how to design "brain" structures that fit within kilobytes of RAM and milliwatts of power.

Beyond algorithmic optimization, the book covers the practicalities of the embedded software stack and hardware acceleration. It explains how to navigate interchange formats like ONNX and TFLite, and how to utilize accelerated inference toolchains such as TVM, TensorRT, and XLA to map high-level graphs to low-level silicon instructions. Detailed chapters on memory footprint management, real-time scheduling via RTOS, and signal processing emphasize the importance of a systems-level approach, ensuring that data preprocessing and post-inference logic are as efficient as the model itself.

The latter portion of the book addresses the operational challenges of maintaining AI in the field. It provides rigorous frameworks for reliability, fault tolerance, and observability through telemetry and logging. Significant attention is given to security and privacy, highlighting hardware roots of trust and the emerging paradigm of federated learning to train models without exposing raw user data. The text also covers the logistics of fleet management, including secure over-the-air (OTA) updates and model versioning, to combat model drift and ensure long-term performance.

Finally, the book situates edge AI within a global context of safety standards and ethical considerations, such as the EU AI Act and bias mitigation. It concludes with a forward-looking perspective on extreme quantization, sparse computing, and the growing compute continuum. Written for embedded engineers and machine learning practitioners alike, the work serves as a practical guide to building dependable, efficient, and autonomous intelligence at the data source.

What You'll Find Inside:

• Learn to balance accuracy, latency, and energy consumption through systematic constraint quantification and trade‑off analysis for edge AI systems.
• Master model compression techniques—quantization (PTQ/QAT), pruning, sparsity, low‑rank factorization, and knowledge distillation—to shrink models while preserving performance.
• Explore the edge hardware landscape (MCUs, DSPs, NPUs, GPUs, FPGAs) and how to co‑design models, runtimes, and accelerators for optimal latency and power efficiency.
• Understand deployment pipelines: interchange formats (ONNX, TFLite, Core ML, TorchScript), graph compilers and operator fusion (TVM, TensorRT, XLA, Glow), and embedded SDKs for reliable on‑device inference.
• Gain end‑to‑end system expertise: memory footprint management, real‑time scheduling, power/energy modeling, reliability/fault tolerance, security/privacy (including federated learning), OTA updates, observability, testing, and fleet management at scale.

Who's It For:

This book is for engineers who build intelligent edge products: embedded developers adding perception to sensor nodes or microcontrollers, machine learning practitioners tasked with delivering low‑latency AI features on mobile or embedded Linux devices, and systems engineers responsible for ensuring reliable, secure, and scalable operation across fleets of edge devices. It assumes familiarity with Python and basic deep learning concepts but does not require prior experience with compilers, real‑time operating systems, or hardware acceleration, providing actionable patterns and checklists to bridge that gap.