NEURAL PROCESSING UNITS: THE COMPLETE GUIDE TO AI ACCELERATION HARDWARE: TOPS Performance, Model Optimization, INT8 Quantization, and Efficient AI Inference for Embedded and Mobile Systems

Understand how NPUs really work and deploy AI models that run faster, leaner, and more reliably on embedded and mobile hardware.AI acceleration is easy to misunderstand when performance claims, toolchains, and quantization strategies all seem to promise more than they deliver. It is even harder to know which numbers matter, which deployment choices hold up in practice, and which hardware paths actually support your model.Neural Processing Units: The Complete Guide to AI Acceleration Hardware gives you a practical, developer-focused understanding of modern AI inference systems. You will learn how NPUs fit into real SoCs, how INT8 quantization and model optimization affect deployment, how runtime stacks and delegates shape real accelerator usage, and how to evaluate performance without being misled by marketing metrics.Understand what NPUs are designed to accelerate, and how they differ from CPUs, GPUs, and DSPsInterpret TOPS, latency, throughput, memory bandwidth, and energy per inference with more confidenceApply INT8 quantization, calibration, range estimation, and accuracy preservation for practical deploymentImprove model efficiency with operator fusion, graph simplification, pruning, weight packing, and hardware-friendly layoutsChoose between post-training quantization and quantization-aware training based on real deployment trade-offsWork through runtime stacks, conversion formats, compilers, delegates, execution providers, and backend pluginsDetect fallback behavior, unsupported operations, layout conversions, and graph partitioning problems before shippingBenchmark NPUs correctly with workload-based tests, cold and warm runs, and sustained performance validationDesign for thermal limits, power budgets, always-on AI, and low-power inference paths in real devicesOptimize vision, audio, sensor-fusion, and transformer workloads for edge and on-device inferenceBuild practical deployment workflows for converting, profiling, validating, packaging, testing, and updating modelsSelect AI acceleration hardware based on model type, deployment goals, ecosystem maturity, and integration riskThis guide also covers on-device generative AI, KV cache behavior, prompt processing, low-bit inference trade-offs, and the system-level decisions that separate a fast demo from a reliable inference product.Throughout the book, working code snippets help you connect the concepts to real deployment tasks, including quantization, profiling, validation, and optimization workflows.If you want a practical guide to AI acceleration hardware that goes beyond surface-level claims and helps you make better deployment decisions, grab your copy today. Read more