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 processingDesign processing elements, compute arrays, and NPU architecturesExplore weight-stationary, output-stationary, and input-stationary dataflowsDesign memory hierarchies around bandwidth, reuse, tiling, and data movementWork with INT8, INT4, mixed precision, fixed-point, and integer arithmeticDesign NPU instruction sets, command queues, registers, and programming modelsTransform neural-network graphs into hardware operationsBuild compiler stages for graph lowering, operator mapping, scheduling, and code generationTranslate an NPU architecture into RTLDesign processing elements, controllers, interfaces, and pipelinesBuild testbenches and reference models for hardware verificationPerform functional, numerical, and performance validationPrototype an NPU on an FPGAAnalyze throughput, latency, bandwidth, utilization, power, and areaOptimize compute, memory, and compiler schedulingUnderstand the path from an educational prototype toward production hardwareWho 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 accelerationFPGA developers interested in neural-network hardwareCompiler engineers working with accelerators and specialized architecturesAI and machine-learning engineers who want to understand the hardware beneath their modelsComputer architecture students and practitionersResearchers exploring NPU and AI accelerator designAdvanced students studying hardware/software co-designEngineers who already know one part of the stack and want to understand the layers around itYou 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.
