PRACTICAL NPU PROGRAMMING: Bridging Machine Learning, Digital Design, and FPGA Systems
Have you ever wondered what actually happens inside a neural network once it moves beyond software and into hardware? How does a trained AI model become a real system running on silicon, FPGAs, and accelerators?
Practical NPU Programming: Bridging Machine Learning, Digital Design, and FPGA Systems by Noah Anderson Schmoe is designed for engineers, developers, and researchers ready to explore the intersection of AI and hardware architecture.
This book takes you from neural network fundamentals and fixed-point quantization to Verilog RTL design, systolic arrays, FPGA integration, and AI accelerator development. Along the way, you'll learn how machine learning models are transformed into efficient hardware systems capable of real-time inference and high-performance computation.
Inside, you'll explore:
Neural Processing Unit (NPU) architecture
FPGA-based AI acceleration
Fixed-point arithmetic and quantization
Verilog RTL implementation
Memory pipelines and dataflow systems
AI inference optimization
Hardware/software co-design principles
More than a programming guide, this book teaches you to think like a systems architect-bridging machine learning theory with digital hardware reality.
If you are ready to move beyond using AI systems and start designing the hardware that powers them, this book is your starting point.
Build alongside the concepts, challenge your assumptions, and discover how modern AI is becoming a hardware revolution as much as a software one.
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