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Embedded Intelligence: Implementing On-Device TinyML on the ESP32
Paperback
General ComputersIndustrial Design
Publisher: Independently Published
Published: Jan 4 2026
Pages: 284
Weight: 1.09
Height: 0.60 Width: 7.00 Depth: 10.00
Language: English
The era of cloud-dependent artificial intelligence is shifting. As the demand for privacy, low latency, and energy efficiency grows, the focus has moved to the very edge of the network. Embedded Intelligence: Implementing On-Device TinyML on the ESP32 is the definitive guide to bringing sophisticated machine learning models to one of the world's most popular microcontrollers.
Written for engineers, hobbyists, and developers, this book provides a rigorous, hands-on exploration of the TinyML lifecycle. You will learn how to transform raw sensor data into actionable intelligence without ever sending a single byte to the cloud. By leveraging the unique dual-core architecture of the ESP32, you will discover how to orchestrate high-speed data acquisition alongside intensive neural network inference.
What you will master inside this book:
- The Hardware Foundation: Deep-dive into the ESP32's Xtensa cores, memory hierarchy, and specialized peripherals like I2S and DMA for seamless data streaming.
- Framework Integration: Step-by-step implementation using TensorFlow Lite for Microcontrollers and Edge Impulse to build, test, and deploy robust models.
- Advanced Optimization: Technical strategies for model pruning, quantization, and memory planning to fit complex intelligence into a tiny SRAM footprint.
- Real-World Applications: Practical projects including wake-word detection, image classification with the ESP32-CAM, and anomaly detection for industrial monitoring.
- Power and Efficiency: Expert techniques for utilizing Deep Sleep, Light Sleep, and the ULP coprocessor to extend battery life for years.
- Hardware Security: Hardening your devices with Secure Boot, Flash Encryption, and authenticated OTA updates to protect your intellectual property.
In the modern landscape of IoT, an intelligent device is only as good as its reliability. This book goes beyond simple code examples to teach you the art of real-time scheduling with FreeRTOS. You will learn how to manage task priorities and implement fail-safe watchdogs to ensure your AI never stalls or crashes in the field.
Whether you are building the next generation of smart home sensors or optimizing industrial machinery, Embedded Intelligence provides the technical depth and practical clarity needed to succeed. Stop relying on expensive cloud APIs and start building truly autonomous, private, and efficient edge devices today.
Equip yourself with the skills to lead the TinyML revolution. Get your copy of Embedded Intelligence today.
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