Jetson Platform Development Guide (eBook, ePUB)
Definitive Reference for Developers and Engineers
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"Jetson Platform Development Guide" The "Jetson Platform Development Guide" is the definitive technical manual for harnessing the full potential of NVIDIA Jetson embedded systems. Addressed to engineers, developers, and system architects, this guide navigates the comprehensive range of Jetson modules-including Nano, TX, Xavier, and Orin-delving deeply into their hardware architectures, performance profiles, and integration strategies. From system-on-module design and expansion interfaces to advanced carrier board considerations and foundational platform security, the book offers thorough insig...
"Jetson Platform Development Guide"
The "Jetson Platform Development Guide" is the definitive technical manual for harnessing the full potential of NVIDIA Jetson embedded systems. Addressed to engineers, developers, and system architects, this guide navigates the comprehensive range of Jetson modules-including Nano, TX, Xavier, and Orin-delving deeply into their hardware architectures, performance profiles, and integration strategies. From system-on-module design and expansion interfaces to advanced carrier board considerations and foundational platform security, the book offers thorough insight into creating robust, scalable Jetson-based solutions.
Beyond hardware, the guide expertly covers the entire software stack: from deploying and customizing Linux for Tegra (L4T) and JetPack SDK to mastering containerized workloads and CI/CD pipelines tailored for edge AI development. Readers are equipped with advanced CUDA programming techniques, memory and data locality optimizations, and best practices for harnessing hardware-accelerated deep learning. Step-by-step methodologies for deploying AI models, leveraging TensorRT, managing precision tuning, and utilizing DLA cores spotlight how to accelerate inference workflows for demanding vision and perception applications.
Further enriching its value, the book addresses low-level device access, real-time processing, and embedded connectivity, providing actionable guidance on driver development, synchronization, and networking. Security and reliability are prioritized through sections on secure boot, encryption, OTA updates, and compliance. Detailed chapters on diagnostics, profiling, power management, and system hardening empower readers to maximize performance and ensure robust deployment. Real-world case studies and future-looking insights round out this essential reference, positioning it as a cornerstone resource for professionals building the next generation of AI-powered edge systems.
The "Jetson Platform Development Guide" is the definitive technical manual for harnessing the full potential of NVIDIA Jetson embedded systems. Addressed to engineers, developers, and system architects, this guide navigates the comprehensive range of Jetson modules-including Nano, TX, Xavier, and Orin-delving deeply into their hardware architectures, performance profiles, and integration strategies. From system-on-module design and expansion interfaces to advanced carrier board considerations and foundational platform security, the book offers thorough insight into creating robust, scalable Jetson-based solutions.
Beyond hardware, the guide expertly covers the entire software stack: from deploying and customizing Linux for Tegra (L4T) and JetPack SDK to mastering containerized workloads and CI/CD pipelines tailored for edge AI development. Readers are equipped with advanced CUDA programming techniques, memory and data locality optimizations, and best practices for harnessing hardware-accelerated deep learning. Step-by-step methodologies for deploying AI models, leveraging TensorRT, managing precision tuning, and utilizing DLA cores spotlight how to accelerate inference workflows for demanding vision and perception applications.
Further enriching its value, the book addresses low-level device access, real-time processing, and embedded connectivity, providing actionable guidance on driver development, synchronization, and networking. Security and reliability are prioritized through sections on secure boot, encryption, OTA updates, and compliance. Detailed chapters on diagnostics, profiling, power management, and system hardening empower readers to maximize performance and ensure robust deployment. Real-world case studies and future-looking insights round out this essential reference, positioning it as a cornerstone resource for professionals building the next generation of AI-powered edge systems.
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