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欢迎来到数字公园

探索计算机体系结构、高性能计算、机器学习系统。

Where nanoseconds meet neurons

📜 A High Performance Computing Explorer’s Atlas | Ongoing Learning Repository

“Standing on the shoulders of giants and occasionally peeking through their notes” — An evolving handbook for hardware-centric learning

📊 Overview

AttributeDetails
StatusActively Curating (Knowledge lava cooling into crystallized, organized notes)
Core FocusVLSI Digital IC Design + High Performance Computing (HPC) + AI System Optimization
Ethical NoteContains reconstructed knowledge from cited sources—strictly for educational purposes
Digital GardenHere is my digital garden

Repository Manifesto

This repository archives my (not well organized) learning journey through VLSI Digital IC design, incorporating:

  • 🧠 Heterogeneous Computing: GPU/FPGA/CGRA workload partitioning
  • ⚙️ AI-Tailored Architectures: Tensor cores to neuromorphic accelerators
  • 🔗 RISC-V Ecosystem: Custom extension development (Vector, AI/ML)
  • 🚨 VLSI-Scale Verification: Formal methods for billion-gate designs

Guiding Principles for AI-Era Learning

In the age of LLMs, permanent learning requires intentional practices to avoid superficial understanding. These four pillars shape the structure of this repository:

  1. Systemic View: Bidirectional insight from top-down (system architecture) and bottom-up (circuit-level details) to connect isolated concepts.
  2. First Principles: Ground all learning in fundamental laws (e.g., digital logic, parallel computing fundamentals) to enable critical thinking.
  3. Hands-On Practice: Prioritize “getting hands dirty”—from code implementation to hardware design—to turn theoretical knowledge into skill.
  4. AI Assistance: Treat LLMs as co-evolutionary tools (not replacements) for accelerating research, debugging, and knowledge synthesis.

🌐 Knowledge Matrix

.
├── 00-ToolKit
│   ├── assets
│   ├── Scripts
│   └── Template
├── 01-ComputerScience
│   ├── Architecture
│   ├── Network
│   ├── OperatingSystem
│   ├── Programming
│   ├── SoC
│   └── SystemBasics
├── 02-AISystem
│   ├── Acceleration
│   ├── AICompiler
│   ├── AISysReview
│   ├── AlgorithmAndModel
│   ├── ClusterAndHardware
│   ├── Distributed
│   ├── GPU
│   ├── HPCBasics
│   ├── JobInterview
│   ├── MLFramework
│   └── Quantization
├── 03-Algorithm
│   ├── BasicAlgorithm
│   ├── Compression
│   ├── Encryption
│   ├── HDC
│   └── Vision
├── 04-IntegratedCircuit
│   ├── Accelerator
│   ├── AsicFlow
│   ├── Basics
│   └── IP
├── 05-SelfDevelopment
│   ├── Career
│   ├── Civilization
│   ├── Language
│   ├── Literature
│   ├── Medicine&Food
│   ├── Philosophy
│   ├── Psychology
│   ├── Reading
│   └── Recreation
└── Educated

🛠️ Usage & Navigation

Highlight

I’ve arranged my notes into 6 parts:

  • 🗂️ 01-ComputerScience: Foundational pillars of computing, from hardware to software.
  • 🗂️ 02-AISystem: Deep dive into the architecture and infrastructure powering AI.
  • 🗂️ 03-Algorithm: Core computational methods and specialized techniques for problem-solving.
  • 🗂️ 04-IntegratedCircuit: Design, flow, and technology behind modern semiconductor chips.
    • A Guide to RISCV CPU architecture
    • Roadmap of YSYX（一生一芯）
  • 🗂️ 05-SelfDevelopment: Resources for personal growth, career, and well-being.
    • Medicine
  • 🗂️ 00-ToolKit: Essential tools, scripts, and assets to boost productivity.
    • Enabling high efficiency using Linux, shell, etc.

  • System of Computer Science

  • AI Full-Stack System Optimization

  • High Performance CPU Design

  • Hardware Accelerator Design

  • Silicon Implementation Flow

🔐 License Matrix

CC BY-NC-SA 4.0

🌟 Roadmap to Enlightenment

Graph View

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