Master the Architecture and Optimization of Edge AI with the Definitive Engineering Guide to NPU Acceleration. Deploying deep learning models to production on Neural Processing Units (NPUs) requires more than standard model tuning. It demands a hardware-aware engineering framework that unifies memory hierarchy, graph optimization, and runtime execution. Neural Processing Unit Acceleration delivers a hands-on, end-to-end systems approach for ML engineers, hardware architects, and embedded systems developers. Learn how to bridge the gap between high-level AI frameworks and raw hardware constraints to achieve maximum performance, lower latency, and optimal power efficiency. Inside This Comprehensive Guide, You Will Discover: - NPU Execution & Memory Hierarchy: Principles of framing tensor engines, managing local SRAM, and optimizing memory bandwidth.- Graph Preparation & Lowering: Step-by-step techniques for graph capture, constant folding, shape inference, and execution lowering.- Operator Mapping & Fallbacks: Strategies for supporting custom ops, fused operations, and managing robust fallback paths.- Quantization Strategies for NPUs: Calibration methods, per-channel scaling, activation ranges, and int8/int4 kernel optimization.- Heterogeneous Execution: Mastering CPU-NPU splits, GPU fallback, and pipeline synchronization.- Profiling & Debugging: Identifying operator hotspots, solving precision drift, and eliminating driver mismatches in production. Whether you are optimizing vision models for mobile chips, edge robotics, or auto-grade SoCs, this book provides clear decision frameworks, practical checklists, and production-ready architectural patterns. Take your edge AI deployments to production speed-scroll up and get your copy today!
