Paddle Lite for Mobile Inference (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Paddle Lite for Mobile Inference" "Paddle Lite for Mobile Inference" is a comprehensive guide that illuminates the technical foundations and practicalities of deploying artificial intelligence at the edge. Beginning with an in-depth exploration of the evolution of mobile AI, the book investigates the unique challenges posed by mobile and embedded hardware, such as stringent latency requirements, bandwidth constraints, and the difficulties of resource management. It provides a broad survey of lightweight deep learning frameworks before positioning Paddle Lite as a powerful, flexible tool withi...
"Paddle Lite for Mobile Inference" "Paddle Lite for Mobile Inference" is a comprehensive guide that illuminates the technical foundations and practicalities of deploying artificial intelligence at the edge. Beginning with an in-depth exploration of the evolution of mobile AI, the book investigates the unique challenges posed by mobile and embedded hardware, such as stringent latency requirements, bandwidth constraints, and the difficulties of resource management. It provides a broad survey of lightweight deep learning frameworks before positioning Paddle Lite as a powerful, flexible tool within the current AI inference ecosystem. Structured for engineers, researchers, and practitioners, the book meticulously details the architecture and design principles that enable Paddle Lite's cross-platform performance and extensibility. Readers are guided through every layer of the system, from model conversion and quantization techniques to backend hardware acceleration for CPUs, GPUs, NPUs, and custom AI chips. The book places special emphasis on end-to-end deployment workflows for both Android and iOS platforms, offering clear guidance on achieving minimal binary footprints, robust integration, and automated profiling on real-world devices. Beyond technical optimization, "Paddle Lite for Mobile Inference" addresses security, privacy, ethical, and regulatory considerations, ensuring that on-device AI is both responsible and compliant. The text concludes with advanced use cases, production-grade case studies, future research directions, and insights into the open-source and developer community. With its blend of foundational knowledge, hands-on guidance, and forward-looking perspectives, this book is an essential resource for anyone building or deploying AI-powered mobile applications.
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