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

Thornton, Mercer

9798178234648: Building Neural Processors: A Hands-On Guide to NPU Architecture, Compiler Design, RTL Implementation, and Hardware Prototyping

Softcover

ISBN 13: 9798178234648

Publisher: Independently published, 2026

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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.

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