SKU/Artículo: AMZ-B0GM182QXW
AI NPU System Design with Python and Verilog: Building from Scratch: A Complete Guide to Modeling, Custom ISA, Compiler, and FPGA Implementation
Disponibilidad:
En stock
En stock
Peso con empaque:
0.64 kg
0.64 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Sobre este producto
- 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.
- Open-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
- Industrial "Bit-Exact" Verification: Master the professional strategies required to achieve a 100% numerical match between software models and hardware logic.
- Architectural Depth: Coverage includes advanced technical topics such as Systolic Array optimization, Fixed-point arithmetic (INT8), and custom Instruction Set Architecture (ISA) design.
- Systolic Array Foundations: Analysis of data reuse strategies and Processing Element (PE) logic.
- Layer Implementation: Hardware mapping for Convolution, Max-Pooling, and Fully-Connected layers.
- Fixed-point Arithmetic: Designing efficient MAC units and scaling logic using bit-shift operations.
- Automated Frameworks: Establishing robust verification environments for 100+ MNIST datasets to ensure comprehensive system reliability.
- 100% Bit-Exact Verification: Ensuring absolute bit-level consistency between Python algorithmic modeling and RTL simulation to guarantee flawless logical integrity.
- End-to-End FPGA Implementation: Transitioning from simulation to physical hardware by implementing MNIST digit recognition on an FPGA, validating the entire NPU design flow.
- Custom ISA Design: Developing a flexible instruction set tailored for AI inference tasks.
- ISA Compiler: Building a Python-based compiler to automate machine code generation.
- Instruction Cycle Logic: Designing control units for instruction fetch, decode, and execution cycles.
- Memory Initialization: A guide to $readmemh and file path management in complex RTL projects.
- Analytical Checklist: Strategies for resolving Simulation-vs-Hardware mismatches during implementation.
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AR$407.512
55% OFF
AR$185.235
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