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NPU Engineering from First Principles: From Neural Network Mathematics to NPU Architecture, Compiler Design, RTL Implementation, and Physical Prototyping - Couverture souple
Synopsis
What if you could understand not just how AI models run—but how the hardware that accelerates them is actually designed?
How does a neural-network operation become a hardware instruction? Why do AI workloads benefit from specialized processors? How do processing elements, tensor engines, memory systems, compilers, and programmable hardware work together to create a functioning NPU?
NPU Engineering from First Principles takes you from these questions to practical NPU system design.
Written for beginners while progressing into professional concepts, this book connects AI computation with the hardware and software required to accelerate it. Rather than studying isolated components, you will learn how the complete system fits together—from neural-network mathematics and architecture to implementation and verification.
Inside, you will learn how to:
Understand modern AI workloads and NPU architecture
Design processing elements, MAC units, matrix engines, and tensor-processing pipelines
Apply quantization, low-precision computing, sparsity, and model optimization
Build Python reference models and NPU behavioral simulators
Design memory hierarchies, buffers, dataflow, streaming, and double-buffering systems
Create a custom NPU instruction set and assembly language
Understand compiler design, tiling, scheduling, memory allocation, and instruction generation
Develop synthesizable RTL for NPU hardware
Prototype and evaluate NPU designs using FPGA hardware
Verify software, instructions, RTL, and hardware systematically
Understand transformer acceleration, attention processing, KV-cache management, edge AI, and multi-core NPUs
Build 15 practical projects, progressing from individual processing elements to a complete programmable NPU system
But why does this matter?
Because designing a successful AI accelerator is not simply about adding more arithmetic units. Performance depends on balancing computation, memory bandwidth, data movement, numerical precision, programmability, and software. This book teaches you to think about those relationships like an accelerator engineer.
Are you a student trying to enter AI hardware? A programmer wondering what happens beneath AI software? An FPGA developer ready for a more ambitious project? Or an aspiring processor engineer looking to build practical skills and stronger portfolio projects?
You don't need to already be an NPU expert. This book starts with the principles and progressively shows you how the pieces connect.
So, are you ready to move from simply using AI technology to understanding how its computing engines are built?
Get your copy of NPU Engineering from First Principles and start developing the skills to model, design, program, verify, and build AI accelerators from the ground up.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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NPU Engineering from First Principles: From Neural Network Mathematics to NPU Architecture, Compiler Design, RTL Implementation, and Physical Prototyping
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NPU Engineering from First Principles (Paperback)
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Paperback. Etat : new. Paperback. What if you could understand not just how AI models run-but how the hardware that accelerates them is actually designed?How does a neural-network operation become a hardware instruction? Why do AI workloads benefit from specialized processors? How do processing elements, tensor engines, memory systems, compilers, and programmable hardware work together to create a functioning NPU?NPU Engineering from First Principles takes you from these questions to practical NPU system design.Written for beginners while progressing into professional concepts, this book connects AI computation with the hardware and software required to accelerate it. Rather than studying isolated components, you will learn how the complete system fits together-from neural-network mathematics and architecture to implementation and verification.Inside, you will learn how to: Understand modern AI workloads and NPU architectureDesign processing elements, MAC units, matrix engines, and tensor-processing pipelinesApply quantization, low-precision computing, sparsity, and model optimizationBuild Python reference models and NPU behavioral simulatorsDesign memory hierarchies, buffers, dataflow, streaming, and double-buffering systemsCreate a custom NPU instruction set and assembly languageUnderstand compiler design, tiling, scheduling, memory allocation, and instruction generationDevelop synthesizable RTL for NPU hardwarePrototype and evaluate NPU designs using FPGA hardwareVerify software, instructions, RTL, and hardware systematicallyUnderstand transformer acceleration, attention processing, KV-cache management, edge AI, and multi-core NPUsBuild 15 practical projects, progressing from individual processing elements to a complete programmable NPU systemBut why does this matter?Because designing a successful AI accelerator is not simply about adding more arithmetic units. Performance depends on balancing computation, memory bandwidth, data movement, numerical precision, programmability, and software. This book teaches you to think about those relationships like an accelerator engineer.Are you a student trying to enter AI hardware? A programmer wondering what happens beneath AI software? An FPGA developer ready for a more ambitious project? Or an aspiring processor engineer looking to build practical skills and stronger portfolio projects?You don't need to already be an NPU expert. This book starts with the principles and progressively shows you how the pieces connect.So, are you ready to move from simply using AI technology to understanding how its computing engines are built?Get your copy of NPU Engineering from First Principles and start developing the skills to model, design, program, verify, and build AI accelerators from the ground up. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798172012396
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Paperback. Etat : new. Paperback. What if you could understand not just how AI models run-but how the hardware that accelerates them is actually designed?How does a neural-network operation become a hardware instruction? Why do AI workloads benefit from specialized processors? How do processing elements, tensor engines, memory systems, compilers, and programmable hardware work together to create a functioning NPU?NPU Engineering from First Principles takes you from these questions to practical NPU system design.Written for beginners while progressing into professional concepts, this book connects AI computation with the hardware and software required to accelerate it. Rather than studying isolated components, you will learn how the complete system fits together-from neural-network mathematics and architecture to implementation and verification.Inside, you will learn how to: Understand modern AI workloads and NPU architectureDesign processing elements, MAC units, matrix engines, and tensor-processing pipelinesApply quantization, low-precision computing, sparsity, and model optimizationBuild Python reference models and NPU behavioral simulatorsDesign memory hierarchies, buffers, dataflow, streaming, and double-buffering systemsCreate a custom NPU instruction set and assembly languageUnderstand compiler design, tiling, scheduling, memory allocation, and instruction generationDevelop synthesizable RTL for NPU hardwarePrototype and evaluate NPU designs using FPGA hardwareVerify software, instructions, RTL, and hardware systematicallyUnderstand transformer acceleration, attention processing, KV-cache management, edge AI, and multi-core NPUsBuild 15 practical projects, progressing from individual processing elements to a complete programmable NPU systemBut why does this matter?Because designing a successful AI accelerator is not simply about adding more arithmetic units. Performance depends on balancing computation, memory bandwidth, data movement, numerical precision, programmability, and software. This book teaches you to think about those relationships like an accelerator engineer.Are you a student trying to enter AI hardware? A programmer wondering what happens beneath AI software? An FPGA developer ready for a more ambitious project? Or an aspiring processor engineer looking to build practical skills and stronger portfolio projects?You don't need to already be an NPU expert. This book starts with the principles and progressively shows you how the pieces connect.So, are you ready to move from simply using AI technology to understanding how its computing engines are built?Get your copy of NPU Engineering from First Principles and start developing the skills to model, design, program, verify, and build AI accelerators from the ground up. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798172012396
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NPU Engineering from First Principles : From Neural Network Mathematics to NPU Architecture, Compiler Design, RTL Implementation, and Physical Prototyping
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Taschenbuch. Etat : Neu. Neuware - What if you could understand not just how AI models run-but how the hardware that accelerates them is actually designed How does a neural-network operation become a hardware instruction Why do AI workloads benefit from specialized processors How do processing elements, tensor engines, memory systems, compilers, and programmable hardware work together to create a functioning NPU NPU Engineering from First Principles takes you from these questions to practical NPU system design.Written for beginners while progressing into professional concepts, this book connects AI computation with the hardware and software required to accelerate it. Rather than studying isolated components, you will learn how the complete system fits together-from neural-network mathematics and architecture to implementation and verification.Inside, you will learn how to: - Understand modern AI workloads and NPU architecture- Design processing elements, MAC units, matrix engines, and tensor-processing pipelines- Apply quantization, low-precision computing, sparsity, and model optimization- Build Python reference models and NPU behavioral simulators- Design memory hierarchies, buffers, dataflow, streaming, and double-buffering systems- Create a custom NPU instruction set and assembly language- Understand compiler design, tiling, scheduling, memory allocation, and instruction generation- Develop synthesizable RTL for NPU hardware- Prototype and evaluate NPU designs using FPGA hardware- Verify software, instructions, RTL, and hardware systematically- Understand transformer acceleration, attention processing, KV-cache management, edge AI, and multi-core NPUs- Build 15 practical projects, progressing from individual processing elements to a complete programmable NPU systemBut why does this matter Because designing a successful AI accelerator is not simply about adding more arithmetic units. Performance depends on balancing computation, memory bandwidth, data movement, numerical precision, programmability, and software. This book teaches you to think about those relationships like an accelerator engineer.Are you a student trying to enter AI hardware A programmer wondering what happens beneath AI software An FPGA developer ready for a more ambitious project Or an aspiring processor engineer looking to build practical skills and stronger portfolio projects You don't need to already be an NPU expert. This book starts with the principles and progressively shows you how the pieces connect.So, are you ready to move from simply using AI technology to understanding how its computing engines are built Get your copy of NPU Engineering from First Principles and start developing the skills to model, design, program, verify, and build AI accelerators from the ground up. N° de réf. du vendeur 9798172012396
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