{"product_id":"b0glqvjwmk","title":"AI NPU System Design with Python and Verilog: Building from Scratch: A Complete Guide to Modeling, Custom ISA, Compiler, and FPGA Implementation","description":"\u003cp\u003eAI NPU System Design with Python and Verilog: A Comprehensive Guide to Hardware-Software Co-Design\u003cbr\u003e\nBridging Mathematical Abstraction and Silicon Reality\u003c\/p\u003e\n\n\u003cp\u003eIn the landscape of modern artificial intelligence, innovation is increasingly driven by the hardware that executes the algorithms.  AI NPU System Design with Python and Verilog  provides a rigorous academic and practical framework for designing a Neural Processing Unit (NPU) from the ground up. This volume is structured to guide researchers, engineers, and students through the complete design cycle—from Python-based modeling to physical Verilog RTL implementation on FPGA hardware.\u003c\/p\u003e\n\n\u003cp\u003e[Key Pedagogical Features] 100+ High-Resolution Color Illustrations : To ensure clarity in explaining complex dataflows and architectures, this book includes over 100 professionally rendered color diagrams. These visuals provide an intuitive understanding of internal hardware operations.\u003c\/p\u003e\n\n\u003cp\u003eOpen-Source Repository : To support hands-on learning, the complete source code—including Python Golden Models, Verilog RTL, and the ISA Compiler—is available at:  https:\/\/github.com\/estlit\/AI_NPU_System_Design_v1\u003c\/p\u003e\n\n\u003cp\u003eIndustrial Bit-Exact Verification : Master the professional strategies required to achieve a 100% numerical match between software models and hardware logic.\u003c\/p\u003e\n\n\u003cp\u003eArchitectural Depth : Coverage includes advanced technical topics such as Systolic Array optimization, Fixed-point arithmetic (INT8), and custom Instruction Set Architecture (ISA) design.\u003c\/p\u003e\n\n\u003cp\u003e[Major Course of Study] (Stage 01) Core NPU Architecture \u0026amp; Unit Design\u003c\/p\u003e\n\n\u003cp\u003eSystolic Array Foundations : Analysis of data reuse strategies and Processing Element (PE) logic.\u003c\/p\u003e\n\n\u003cp\u003eLayer Implementation : Hardware mapping for Convolution, Max-Pooling, and Fully-Connected layers.\u003c\/p\u003e\n\n\u003cp\u003eFixed-point Arithmetic : Designing efficient MAC units and scaling logic using bit-shift operations.\u003c\/p\u003e\n\n\u003cp\u003e(Stage 02) System Validation \u0026amp; Mass Verification\u003c\/p\u003e\n\n\u003cp\u003eAutomated Frameworks:  Establishing robust verification environments for 100+ MNIST datasets to ensure comprehensive system reliability.\u003c\/p\u003e\n\n\u003cp\u003e100% Bit-Exact Verification:  Ensuring absolute bit-level consistency between Python algorithmic modeling and RTL simulation to guarantee flawless logical integrity.\u003c\/p\u003e\n\n\u003cp\u003eEnd-to-End FPGA Implementation:  Transitioning from simulation to physical hardware by implementing MNIST digit recognition on an FPGA, validating the entire NPU design flow.\u003c\/p\u003e\n\n\u003cp\u003e(Stage 03) Programmable ISA Processor System\u003c\/p\u003e\n\n\u003cp\u003eCustom ISA Design : Developing a flexible instruction set tailored for AI inference tasks.\u003c\/p\u003e\n\n\u003cp\u003eISA Compiler : Building a Python-based compiler to automate machine code generation.\u003c\/p\u003e\n\n\u003cp\u003eInstruction Cycle Logic : Designing control units for instruction fetch, decode, and execution cycles.\u003c\/p\u003e\n\n\u003cp\u003e(Stage 04) Technical Troubleshooting \u0026amp; Path Configuration\u003c\/p\u003e\n\n\u003cp\u003eMemory Initialization : A guide to $readmemh and file path management in complex RTL projects.\u003c\/p\u003e\n\n\u003cp\u003eAnalytical Checklist : Strategies for resolving Simulation-vs-Hardware mismatches during implementation.\u003c\/p\u003e\n\n\u003cp\u003e[Who This Volume is For] This book is intended for  Computer Science and Electronic Engineering students ,  Hardware Design Engineers , and  AI Researchers  who seek a profound understanding of how neural networks are physically executed at the gate level. It serves as an essential reference for those transitioning from algorithm users to hardware architects.\u003c\/p\u003e\n\n\u003cp\u003eMaster the silicon foundations of artificial intelligence. Transition from abstract code to verified hardware reality.\u003c\/p\u003e","brand":"ANT","offers":[{"title":"Ebook","offer_id":67938209103908,"sku":"YSL42795","price":7.79,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0805\/2614\/7620\/files\/71e1zYpahML._SY1500.jpg?v=1793282876","url":"https:\/\/booktavero.com\/products\/b0glqvjwmk","provider":"BookTavero","version":"1.0","type":"link"}