Embedded Artificial Intelligence
Embedded Artificial Intelligence (AI) refers to the integration of AI capabilities directly into hardware devices, enabling them to perform intelligent tasks locally without relying heavily on external computing resources. This fusion allows machines such as sensors, wearables, IoT devices, and autonomous systems to process data, make decisions, and adapt in real-time, often without needing a constant internet connection. Embedded AI leverages specialized chips like edge AI processors, neural processing units (NPUs), and microcontrollers with built-in machine learning capabilities. The primary advantage of Embedded AI is its real-time responsiveness, low latency, and improved privacy, as data processing happens on the device itself rather than being sent to cloud servers. It plays a vital role in applications like smart home systems, autonomous vehicles, industrial automation, healthcare monitoring, and mobile robotics. Power efficiency and compact design are critical, making embedded AI ideal for constrained environments. As AI models become more lightweight and hardware more efficient, the scope of embedded AI continues to expand. It is revolutionizing the way everyday devices function-making them more intelligent, context-aware, and autonomous. The growth of 5G, edge computing, and AI hardware accelerators further enhances the capabilities of Embedded AI across various industries. This book explores the integration of artificial intelligence into embedded systems, enabling real-time, intelligent decision-making at the edge. Contents: 1. Embedded Artificial Intelligence: A Comprehensive Overview, 2. Introduction to Embedded Systems, 3. Edge AI: The Future of Artificial Intelligence in Embedded Systems, 4. Hardware Architectures for Embedded AI, 5. Embedded Artificial Intelligence and Machine Learning, 6. Artificial Intelligence in Recurrent Neural Network, 7. Embedded Artificial Intelligence and Robotics.