Building Neural Processors: A Hands-On Guide to NPU Architecture, Compiler Design, RTL Implementation, and Hardware Prototyping

What does it really take to build a neural processing unit?

Not just a matrix multiplier. Not just a systolic array. And not just a neural network running on an accelerator.

A practical NPU sits at the intersection of machine learning, computer architecture, compiler engineering, digital design, RTL development, memory systems, and hardware prototyping. The difficult part is making all of these layers work together.

Building Neural Processors takes you through that journey from the fundamentals of neural-network computation to a working NPU architecture, compiler, RTL implementation, verification environment, and FPGA prototype.

Rather than treating the NPU as a black box, this book shows how the pieces fit together—and why the engineering decisions behind them matter.

You will explore how neural-network operations become hardware workloads, how processing elements execute those workloads, how data moves through memory hierarchies, how quantization affects hardware efficiency, and how compiler decisions ultimately determine what the hardware actually does.

You will also learn how to move beyond architectural diagrams and into implementation.

Inside the Book, You Will Learn How To:

• Understand the computational foundations behind neural processing
• Design processing elements, compute arrays, and NPU architectures
• Explore weight-stationary, output-stationary, and input-stationary dataflows
• Design memory hierarchies around bandwidth, reuse, tiling, and data movement
• Work with INT8, INT4, mixed precision, fixed-point, and integer arithmetic
• Design NPU instruction sets, command queues, registers, and programming models
• Transform neural-network graphs into hardware operations
• Build compiler stages for graph lowering, operator mapping, scheduling, and code generation
• Translate an NPU architecture into RTL
• Design processing elements, controllers, interfaces, and pipelines
• Build testbenches and reference models for hardware verification
• Perform functional, numerical, and performance validation
• Prototype an NPU on an FPGA
• Analyze throughput, latency, bandwidth, utilization, power, and area
• Optimize compute, memory, and compiler scheduling
• Understand the path from an educational prototype toward production hardware

Who Is This Book For?

This book is written for engineers and advanced technical readers who want to understand how neural processors are actually built.

It is particularly useful for:

• Hardware and digital design engineers moving into AI acceleration
• FPGA developers interested in neural-network hardware
• Compiler engineers working with accelerators and specialized architectures
• AI and machine-learning engineers who want to understand the hardware beneath their models
• Computer architecture students and practitioners
• Researchers exploring NPU and AI accelerator design
• Advanced students studying hardware/software co-design
• Engineers who already know one part of the stack and want to understand the layers around it

You do not need to be an expert in every subject covered. A working foundation in digital logic, Verilog/SystemVerilog, compiler concepts, computer architecture, or applied machine learning is enough to begin connecting the pieces.

If you want to move beyond using AI accelerators and start understanding how neural processors themselves are designed and built, this book provides a practical path from neural-network computation to hardware implementation.