The Next-Gen Mobile AI Architectures: Edge AI, On-Device Learning & Beyond

The purpose of this book is to provide a comprehensive understanding of mobile AI, focusing on its evolution, challenges, and cutting-edge techniques that enable its implementation on mobile devices. By examining the latest trends and methodologies in on-device learning, model optimization, and edge computing, the book aims to: Demystify Mobile AI: Explain how AI models are deployed and optimized to work on resource-constrained mobile hardware without compromising performance. Highlight Innovations: Focus on the advancements in Generative AI, transformer-based architectures, and realtime inference that make mobile AI applications more intelligent and responsive. Offer Practical Insights: Provide real-world case studies and implementation strategies for deploying AI models on mobile platforms, including AR/VR applications, speech recognition, and image processing. Guide Future Trends: Discuss emerging trends like federated learning, privacy-preserving AI, and AI model compression, offering a vision for the future of mobile AI beyond its current capabilities. Ultimately, this book seeks to bridge the gap between theoretical AI concepts and practical applications, helping engineers, developers, and researchers understand how to implement and optimize AI in mobile environments.