XNNPACK for Efficient Neural Network Inference on CPU (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"XNNPACK for Efficient Neural Network Inference on CPU" "XNNPACK for Efficient Neural Network Inference on CPU" is an in-depth guide for practitioners and researchers seeking to unlock the full potential of modern CPUs for neural network inference. Beginning with a thorough exploration of CPU microarchitectures, inference workload patterns, and the challenges inherent in traditional CPU-based deep learning, this book provides a nuanced foundation in performance benchmarking, quantization techniques, and the comparative analysis of leading inference frameworks. Readers are equipped to understan...
"XNNPACK for Efficient Neural Network Inference on CPU" "XNNPACK for Efficient Neural Network Inference on CPU" is an in-depth guide for practitioners and researchers seeking to unlock the full potential of modern CPUs for neural network inference. Beginning with a thorough exploration of CPU microarchitectures, inference workload patterns, and the challenges inherent in traditional CPU-based deep learning, this book provides a nuanced foundation in performance benchmarking, quantization techniques, and the comparative analysis of leading inference frameworks. Readers are equipped to understand both the theory and practice of computational bottlenecks and optimization in real-world scenarios. Central to this book is a comprehensive treatment of XNNPACK, a state-of-the-art library designed to maximize neural network inference performance across diverse CPU platforms. From its core design rationale, modular architecture, and support for architectures like x86, ARM, and WebAssembly SIMD, to practical guidance on installation, configuration, and framework integration, each chapter demystifies crucial implementation strategies. Detailed coverage of operator kernels-including convolution, activation, pooling, and quantized operations-empowers readers to build, extend, and fine-tune inference engines that deliver on both speed and resource efficiency. The book concludes with advanced topics essential for production-grade deployment: low-level CPU optimization, threading, energy efficiency, security, and robustness. It also covers XNNPACK's integration with major deep learning frameworks such as TensorFlow Lite, PyTorch Mobile, and ONNX Runtime, and provides actionable insights into model deployment, benchmarking, and field monitoring. Guidance for open source contribution, customization for proprietary hardware, and thoughtful discussion on emerging trends and future research directions make this volume indispensable for professionals seeking to deploy, accelerate, and secure neural network inference on edge, mobile, and server-class CPUs.
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