Embedded Artificial Intelligence: Real-Life Applications and Case Studies

This book explores the role of embedded AI in revolutionizing industries such as healthcare, transportation, manufacturing, and retail. It begins by introducing the fundamentals of AI and embedded systems and specific challenges and opportunities. A key focus of this book is developing efficient and effective algorithms and models for embedded AI systems, as embedded systems have limited processing power, memory, and storage. It discusses a variety of techniques for optimizing algorithms and models for embedded systems, including hardware acceleration, model compression, and quantization.

Key features:

• Explores security experiments in emerging post‑CMOS technologies using AI, including side channel attack‑resistant embedded systems
• Discusses different hardware and software platforms available for developing embedded AI applications, as well as the various techniques used to design and implement these systems
• Considers ethical and societal implications of embedded AI vis‑a‑vis the need for responsible development and deployment of embedded AI systems
• Focuses on application‑based research and case studies to develop embedded AI systems for real‑life applications
• Examines high‑end parallel systems to run complex AI algorithms and comprehensive functionality while maintaining portability and power efficiency

This reference book is for students, researchers, and professionals interested in embedded AI and relevant branches of computer science, electrical engineering, or artificial intelligence.