一、软件介绍

文末提供程序和源码下载

Z-Ant （Zig-Ant）是一个全面的开源神经网络框架，专门用于在微控制器和边缘设备上部署优化的 AI 模型。Z-Ant 使用 Zig 构建，为资源受限的硬件上的模型优化、代码生成和实时推理提供端到端工具。

二、全面的模型部署

ONNX Model Support: Full compatibility with ONNX format models
ONNX 模型支持：与 ONNX 格式模型完全兼容
Cross-platform Compilation: ARM Cortex-M, RISC-V, x86, and more
跨平台编译：ARM Cortex-M、RISC-V、x86 等
Static Library Generation: Generate optimized static libraries for any target architecture
静态库生成：为任何目标架构生成优化的静态库
Real-time Inference: Microsecond-level prediction times on microcontrollers
实时推理：微控制器上的微秒级预测时间

三、高级优化引擎

Quantization: Automatic model quantization with dynamic and static options
量化：具有动态和静态选项的自动模型量化
Pruning: Neural network pruning for reduced model size
修剪：用于减小模型大小的神经网络修剪
Buffer Optimization: Memory-efficient tensor operations
缓冲区优化：节省内存的张量运算
Flash vs RAM Execution: Configurable execution strategies
Flash 与 RAM 执行：可配置的执行策略

四、GUI Interface 🖥 GUI 界面

Z-Ant includes an experimental cross-platform GUI built with SDL for basic model selection and code generation. Note that the GUI is currently unstable and under active development - we recommend using the command-line interface for production workflows.
Z-Ant 包括一个使用 SDL 构建的实验性跨平台 GUI，用于基本模型选择和代码生成。请注意，GUI 当前不稳定，正在积极开发中 - 我们建议对生产工作流使用命令行界面。

📷 ImageToTensor Processing
📷 ImageToTensor 处理

JPEG Decoding: Complete JPEG image processing pipeline
JPEG 解码：完整的 JPEG 图像处理管道
Multiple Color Spaces: RGB, YUV, Grayscale support
多个色彩空间：支持 RGB、YUV、灰度
Hardware Optimization: SIMD and platform-specific optimizations
硬件优化：SIMD 和特定于平台的优化
Preprocessing Pipeline: Normalization, resizing, and format conversion
预处理管道：规范化、调整大小和格式转换

🔧 Extensive ONNX Support
🔧 广泛的 ONNX 支持

30+ Operators: Comprehensive coverage of neural network operations
30+ 算子：神经网络作全面覆盖
Multiple Data Types: Float32, Int64, Bool, and more
多种数据类型：Float32、Int64、Bool 等
Dynamic Shapes: Support for variable input dimensions
动态形状：支持可变输入尺寸
Custom Operators: Extensible operator framework
自定义运算符：可扩展的运算符框架

Why Z-Ant? 为什么选择 Z-Ant？

🚫 Lack of DL Support: Devices like TI Sitara, Raspberry Pi Pico, or ARM Cortex-M lack comprehensive DL libraries
🚫 缺乏 DL 支持：TI Sitara、Raspberry Pi Pico 或 ARM Cortex-M 等设备缺乏全面的 DL 库
🌍 Open-source: Complete end-to-end NN deployment and optimization solution
🌍 开源：完整的端到端 NN 部署和优化解决方案
🎓 Research-Inspired: Implements cutting-edge optimization techniques inspired by MIT's Han Lab research
🎓 受研究启发：实施受麻省理工学院 Han 实验室研究启发的尖端优化技术
🏛 Academic Collaboration: Developed in collaboration with institutions like Politecnico di Milano
🏛 学术合作：与米兰理工大学等机构合作开发
⚡ Performance First: Designed for real-time inference with minimal resource usage
⚡ 性能优先：专为实时推理而设计，资源使用量最少
🔧 Developer Friendly: Clear APIs, extensive documentation, and practical examples
🔧 开发人员友好：清晰的 API、广泛的文档和实际示例

Use Cases 使用案例

🏭 Edge AI: Real-time anomaly detection, predictive maintenance
🏭 Edge AI：实时异常检测，预测性维护
🤖 IoT & Autonomous Systems: Lightweight AI models for drones, robots, vehicles, IoT devices
🤖 物联网和自主系统：用于无人机、机器人、车辆、物联网设备的轻量级人工智能模型
📱 Mobile Applications: On-device inference for privacy-preserving AI
📱 移动应用程序：用于隐私保护 AI 的设备端推理
🏥 Medical Devices: Real-time health monitoring and diagnostics
🏥 医疗设备：实时健康监测和诊断
🎮 Gaming: AI-powered gameplay enhancement on embedded systems
🎮 游戏：嵌入式系统上 AI 驱动的游戏增强功能

同类最佳 TinyML 引擎路线图

To establish Z-Ant as the premier tinyML inference engine, we are pursuing several key improvements:
为了将 Z-Ant 确立为首屈一指的 tinyML 推理引擎，我们正在寻求几项关键改进：

🔥 Performance Optimizations
🔥 性能优化

Ultra-Low Latency Inference
超低延迟推理

Custom Memory Allocators: Zero-allocation inference with pre-allocated memory pools
自定义内存分配器：使用预分配的内存池进行零分配推理
In-Place Operations: Minimize memory copies through tensor operation fusion
就地作：通过张量运算融合最大限度地减少内存副本
SIMD Vectorization: ARM NEON, RISC-V Vector extensions, and x86 AVX optimizations
SIMD 矢量化：ARM NEON、RISC-V 矢量扩展和 x86 AVX 优化
Assembly Kernels: Hand-optimized assembly for critical operations (matrix multiplication, convolution)
汇编内核：针对关键运算（矩阵乘法、卷积）的手动优化汇编
Cache-Aware Algorithms: Memory access patterns optimized for L1/L2 cache efficiency
高速缓存感知算法：针对 L1/L2 高速缓存效率优化的内存访问模式

Advanced Model Optimization
高级模型优化

Dynamic Quantization: Runtime precision adjustment based on input characteristics
动态量化：根据输入特性进行运行时精度调整
Structured Pruning: Channel and block-level pruning for hardware-friendly sparsity
结构化修剪：通道和块级修剪，实现硬件友好的稀疏性
Knowledge Distillation: Automatic teacher-student model compression pipeline
知识蒸馏：自动师生模型压缩管道
Neural Architecture Search (NAS): Hardware-aware model architecture optimization
神经架构搜索（NAS）：硬件感知模型架构优化
Binary/Ternary Networks: Extreme quantization for ultra-low power inference
二进制/三元网络：用于超低功耗推理的极端量化

⚡ Hardware Acceleration ⚡ 硬件加速

Microcontroller-Specific Optimizations
微控制器特定的优化

DSP Instruction Utilization: Leverage ARM Cortex-M DSP instructions and RISC-V packed SIMD
DSP 指令利用：利用 ARM Cortex-M DSP 指令和 RISC-V 封装的 SIMD
DMA-Accelerated Operations: Offload data movement to DMA controllers
DMA 加速作：将数据移动卸载到 DMA 控制器
Flash Execution Strategies: XIP (Execute-in-Place) optimization for flash-resident models
Flash 执行策略：针对 Flash 驻留模型的 XIP（就地执行）优化
Low-Power Modes: Dynamic frequency scaling and sleep mode integration
低功耗模式：动态频率调节和休眠模式集成
Hardware Security Modules: Secure model storage and execution
硬件安全模块：安全的模型存储和执行

Emerging Hardware Support
新兴硬件支持

NPU Integration: Support for dedicated neural processing units (e.g., Arm Ethos, Intel Movidius)
NPU 集成：支持专用神经处理单元（例如 Arm Ethos、Intel Movidius）
FPGA Acceleration: Custom hardware generation for ultra-performance inference
FPGA 加速：用于超高性能推理的定制硬件生成
GPU Compute: OpenCL/CUDA kernels for edge GPU acceleration
GPU 计算：用于边缘 GPU 加速的 OpenCL/CUDA 内核
Neuromorphic Computing: Spike-based neural network execution
Neuromorphic Computing：基于尖峰的神经网络执行

🧠 Advanced AI Capabilities
🧠 高级 AI 功能

Model Compression & Acceleration
模型压缩和加速

Lottery Ticket Hypothesis: Sparse subnetwork discovery and training
彩票假说：稀疏子网络发现和训练
Progressive Quantization: Gradual precision reduction during training/deployment
渐进式量化：训练/部署期间逐渐降低精度
Magnitude-Based Pruning: Automatic weight importance analysis
基于量级的修剪：自动权重重要性分析
Channel Shuffling: Network reorganization for efficient inference
通道改组：网络重组以实现高效推理
Tensor Decomposition: Low-rank approximation for parameter reduction
张量分解：用于参数缩减的低秩近似

Adaptive Inference 自适应推理

Early Exit Networks: Conditional computation based on input complexity
Early Exit Networks：基于输入复杂度的条件计算
Dynamic Model Selection: Runtime model switching based on resource availability
动态模型选择：基于资源可用性的运行时模型切换
Cascaded Inference: Multi-stage models with progressive complexity
级联推理：具有渐进复杂性的多阶段模型
Attention Mechanism Optimization: Efficient transformer and attention implementations
注意力机制优化：高效的 transformer 和 attention 实现

🔧 Developer Experience & Tooling
🔧 开发者体验和工具

Advanced Profiling & Analysis
高级分析和分析

Hardware Performance Counters: Cycle-accurate performance measurement
硬件性能计数器：周期精确的性能测量
Energy Profiling: Power consumption analysis per operation
能量分析：每个作的功耗分析
Memory Footprint Analysis: Detailed RAM/Flash usage breakdown
内存占用分析：详细的 RAM/Flash 使用情况明细
Thermal Analysis: Temperature impact on inference performance
热分析：温度对推理性能的影响
Real-Time Visualization: Live performance monitoring dashboards
实时可视化：实时性能监控仪表板

Automated Optimization Pipeline
自动优化管道

AutoML Integration: Automated hyperparameter tuning for target hardware
AutoML 集成：目标硬件的自动超参数调整
Benchmark-Driven Optimization: Continuous performance regression testing
基准测试驱动优化：持续性能回归测试
Hardware-in-the-Loop Testing: Automated testing on real hardware platforms
硬件在环测试：在真实硬件平台上进行自动化测试
Model Validation: Accuracy preservation verification throughout optimization
模型验证：在整个优化过程中保持精度验证
Deploy-to-Production Pipeline: One-click deployment to embedded systems
部署到生产管道：一键部署到嵌入式系统

🌐 Ecosystem & Integration
🌐 生态系统与集成

Framework Interoperability
框架互作性

TensorFlow Lite Compatibility: Seamless migration from TFLite models
TensorFlow Lite 兼容性：从 TFLite 模型无缝迁移
PyTorch Mobile Integration: Direct PyTorch model deployment pipeline
PyTorch 移动集成：直接 PyTorch 模型部署管道
ONNX Runtime Parity: Feature-complete ONNX runtime alternative
ONNX 运行时奇偶校验：功能齐全的 ONNX 运行时替代方案
MLflow Integration: Model versioning and experiment tracking
MLflow 集成：模型版本控制和实验跟踪
Edge Impulse Compatibility: Integration with popular edge ML platforms
Edge Impulse 兼容性：与流行的边缘 ML 平台集成

Production Deployment 生产部署

OTA Model Updates: Over-the-air model deployment and versioning
OTA 模型更新：无线模型部署和版本控制
A/B Testing Framework: Safe model rollout with performance comparison
A/B 测试框架：安全推出模型并进行性能比较
Federated Learning Support: Distributed training on edge devices
联邦学习支持：边缘设备上的分布式训练
Model Encryption: Secure model storage and execution
模型加密：安全的模型存储和执行
Compliance Tools: GDPR, HIPAA, and safety-critical certifications
合规性工具：GDPR、HIPAA 和安全关键认证

📊 Benchmarking & Validation
📊 基准测试和验证

Industry-Standard Benchmarks
行业标准基准

MLPerf Tiny: Competitive performance on standard benchmarks
MLPerf Tiny：在标准基准测试中具有竞争力的性能
EEMBC MLMark: Energy efficiency measurements
EEMBC MLMark：能效测量
Custom TinyML Benchmarks: Domain-specific performance evaluation
自定义 TinyML 基准测试：特定领域的性能评估
Real-World Workload Testing: Production-representative model validation
真实工作负载测试：具有生产代表性的模型验证
Cross-Platform Consistency: Identical results across all supported hardware
跨平台一致性：在所有支持的硬件上获得相同的结果

Quality Assurance 质量保证

Fuzzing Infrastructure: Automated testing with random inputs
模糊测试基础设施：使用随机输入进行自动化测试
Formal Verification: Mathematical proof of correctness for critical operations
形式化验证：关键作正确性的数学证明
Hardware Stress Testing: Extended operation under extreme conditions
硬件压力测试：在极端条件下延长作时间
Regression Test Suite: Comprehensive backward compatibility testing
回归测试套件：全面的向后兼容性测试
Performance Monitoring: Continuous integration with performance tracking
性能监控：与性能跟踪持续集成

🚀 Getting Started for Contributors
🚀 投稿人入门

Prerequisites 先决条件

Zig Compiler: Install the latest Zig compiler
Zig 编译器：安装最新的 Zig 编译器
Git: For version control and collaboration
Git：用于版本控制和协作
Basic Zig Knowledge: Improve Zig proficiency via Ziglings
Zig 基础知识：通过 Ziglings 提高 Zig 熟练度

Quick Setup 快速设置

Clone the repository: 克隆存储库：

git clone https://github.com/ZIGTinyBook/Z-Ant.git
cd Z-Ant

Run tests to verify setup:
运行测试以验证设置：
```
zig build test --summary all
```
Generate code for a model:
为模型生成代码：
```
zig build codegen -Dmodel=mnist-1
```

First Time Contributors 首次贡献者

Start here if you're new to Z-Ant:
如果您是 Z-Ant 的新用户，请从这里开始：

Run existing tests: Use zig build test --summary all to understand the codebase
运行现有测试：用于 zig build test --summary all 了解代码库
Try code generation: Use zig build codegen -Dmodel=mnist-1 to see the workflow
尝试代码生成：用于 zig build codegen -Dmodel=mnist-1 查看工作流程
Read the documentation: Check /docs/ folder for detailed guides
阅读文档：检查 /docs/ 文件夹以获取详细指南
Review the Hackathon Guide: For specific guidance on the rendering and lowering pipeline, refer to the HackathonGuide.md.
查看 Hackathon 指南：有关渲染和降低管道的具体指导，请参阅 HackathonGuide.md。

Project Architecture 项目架构

<span style="background-color:var(--bgColor-muted, var(--color-canvas-subtle))"><span style="color:#1f2328"><span style="color:var(--fgColor-default, var(--color-fg-default))"><span style="background-color:var(--bgColor-muted, var(--color-canvas-subtle))"><code>Z-Ant/
├── src/                    # Core source code
│   ├── Core/              # Neural network core functionality
│   ├── CodeGen/           # Code generation engine
│   ├── ImageToTensor/     # Image preprocessing pipeline
│   ├── onnx/              # ONNX model parsing
│   └── Utils/             # Utilities and helpers
├── tests/                 # Comprehensive test suite
├── datasets/              # Sample models and test data
├── generated/             # Generated code output
├── examples/              # Arduino and microcontroller examples
└── docs/                  # Documentation and guides
</code></span></span></span></span>

🛠️ Development Workflow 🛠️ 开发工作流程

Quick Start Commands 快速启动命令

# Run comprehensive tests
zig build test --summary all# Generate code for a specific model
zig build codegen -Dmodel=mnist-1# Test generated code
zig build test-codegen -Dmodel=mnist-1# Compile static library for deployment
zig build lib -Dmodel=mnist-1 -Dtarget=thumb-freestanding -Dcpu=cortex_m33

Git Branching Strategy Git 分支策略

We follow a structured branching strategy to ensure code quality and smooth collaboration:
我们遵循结构化的分支策略来确保代码质量和顺利协作：

Branch Types 分支类型

main: Stable, production-ready code for releases
main ：用于发布的稳定、生产就绪代码
feature/<feature-name>: New features under development
feature/<feature-name> ：正在开发的新功能
fix/<issue-description>: Bug fixes and patches
fix/<issue-description> ：错误修复和补丁
docs/<documentation-topic>: Documentation improvements
docs/<documentation-topic> ：文档改进
test/<test-improvements>: Test suite enhancements
test/<test-improvements> ：测试套件增强功能

Best Practices for Contributors
贡献者的最佳实践

Test Before Committing: Run zig build test --summary all before every commit
Test Before Committing：在每次提交之前运行 zig build test --summary all
Document Your Code: Follow Zig's doc-comments standard
记录您的代码：遵循 Zig 的文档注释标准
Small, Focused PRs: Keep pull requests small and focused on a single feature/fix
小型、专注的 PR：保持拉取请求较小并专注于单个功能/修复
Use Conventional Commits: Follow commit message conventions (feat:, fix:, docs:, etc.)
使用常规提交：遵循提交消息约定（feat：、fix：、docs：等）

Using Z-Ant 🔧 使用 Z-Ant

Development Requirements 开发要求

Install the latest Zig compiler
安装最新的 Zig 编译器
Improve Zig proficiency via Ziglings
通过 Ziglings 提高 Zig 熟练度

Running Tests 运行测试

Add tests to build.zig/test_list.
将测试添加到 build.zig/test_list 。

Regular tests: 定期测试：
```
zig build test --summary all
```
Heavy computational tests:
繁重的计算测试：
```
zig build test -Dheavy --summary all
```

Generating Code for Models
为模型生成代码

zig build codegen -Dmodel=model_name [-Dlog -Duser_tests=user_tests.json]

Generated code will be placed in:
生成的代码将被放置在：

<span style="background-color:var(--bgColor-muted, var(--color-canvas-subtle))"><span style="color:#1f2328"><span style="color:var(--fgColor-default, var(--color-fg-default))"><span style="background-color:var(--bgColor-muted, var(--color-canvas-subtle))"><code>generated/model_name/
├── lib_{model_name}.zig
├── test_{model_name}.zig
└── user_tests.json
</code></span></span></span></span>

Testing Generated Models 测试生成的模型

zig build test-codegen -Dmodel=model_name

Integrating into Your Project
集成到您的项目中

Build the static library:
构建静态库：

zig build lib -Dmodel=model_name -Dtarget={arch} -Dcpu={cpu}

Linking with CMake: 与 CMake 链接：

target_link_libraries(your_project PUBLIC path/to/libzant.a)

Logging (Optional) 日志记录（可选）

To set a custom log function from your C code:
要从 C 代码设置自定义日志函数：

extern void setLogFunction(void (*log_function)(uint8_t *string));

🏗️ Build System (`build.zig`)
🏗️ 构建系统（ `build.zig` ）

Available Build Commands 可用的构建命令

Core Commands 核心命令

Standard build: 标准版本：

zig build                                    # Build all targets

Run unit tests: 运行单元测试：

zig build test --summary all                # Run all unit tests

Code generation: 代码生成：

zig build codegen -Dmodel=model_name        # Generate code for specified model

Static library compilation:
静态库编译：

zig build lib -Dmodel=model_name            # Compile static library for deployment

Testing Commands 测试命令

Test generated library: 测试生成的库：

zig build test-generated-lib -Dmodel=model_name    # Test specific generated model library

OneOp model testing: OneOp 模型测试：

zig build test-codegen-gen                   # Generate oneOperation test models
zig build test-codegen                       # Test all generated oneOperation models

ONNX parser testing: ONNX 解析器测试：

zig build onnx-parser                        # Test ONNX parser functionality

Profiling & Performance 分析和性能

Build main executable for profiling:
构建用于性能分析的主可执行文件：

zig build build-main -Dmodel=model_name      # Build profiling target executable

Command-Line Options 命令行选项

Target & Architecture Options
目标和架构选项

-Dtarget=<arch>: Target architecture (e.g., thumb-freestanding, native)
-Dtarget=<arch> ：目标架构（例如、 thumb-freestanding native 、）
-Dcpu=<cpu>: CPU model (e.g., cortex_m33, cortex_m4)
-Dcpu=<cpu> ：CPU 型号（例如、 cortex_m33 、） cortex_m4

Model & Path Options 模型和路径选项

-Dmodel=<name>: Model name (default: mnist-8)
-Dmodel=<name> ：模型名称（默认值： mnist-8 ）
-Dmodel_path=<path>: Custom ONNX model path
-Dmodel_path=<path> ：自定义 ONNX 模型路径
-Dgenerated_path=<path>: Output directory for generated code
-Dgenerated_path=<path> ：生成代码的输出目录
-Doutput_path=<path>: Output directory for compiled library
-Doutput_path=<path> ：编译库的输出目录

Code Generation Options 代码生成选项

-Dlog=true|false: Enable detailed logging during code generation
-Dlog=true|false ：在代码生成期间启用详细日志记录
-Duser_tests=<path>: Specify custom user tests JSON file
-Duser_tests=<path> ：指定自定义用户测试 JSON 文件
-Dshape=<shape>: Input tensor shape
-Dshape=<shape> ：输入张量形状
-Dtype=<type>: Input data type (default: f32)
-Dtype=<type> ：输入数据类型（默认值： f32 ）
-Dcomm=true|false: Generate code with comments
-Dcomm=true|false ：生成带注释的代码
-Ddynamic=true|false: Enable dynamic memory allocation
-Ddynamic=true|false ：启用动态内存分配

Testing Options 测试选项

-Dheavy=true|false: Run heavy computational tests
-Dheavy=true|false ：运行繁重的计算测试
-Dtest_name=<name>: Run specific test by name
-Dtest_name=<name> ：按名称运行特定测试

Debug & Profiling Options
调试和分析选项

-Dtrace_allocator=true|false: Use tracing allocator for debugging (default: true)
-Dtrace_allocator=true|false ：使用跟踪分配器进行调试（默认值： true ）
-Dallocator=<type>: Allocator type to use (default: raw_c_allocator)
-Dallocator=<type> ：要使用的分配器类型（默认值： raw_c_allocator ）

Common Usage Examples 常见使用示例

# Generate code for MNIST model with logging
zig build codegen -Dmodel=mnist-1 -Dlog=true# Build static library for ARM Cortex-M33
zig build lib -Dmodel=mnist-1 -Dtarget=thumb-freestanding -Dcpu=cortex_m33# Test with heavy computational tests enabled
zig build test -Dheavy=true --summary all# Generate code with custom paths and comments
zig build codegen -Dmodel=custom_model -Dmodel_path=my_models/custom.onnx -Dgenerated_path=output/ -Dcomm=true# Build library with custom output location
zig build lib -Dmodel=mnist-1 -Doutput_path=/path/to/deployment/# Run specific test
zig build test -Dtest_name=tensor_math_test# Build profiling executable for performance analysis
zig build build-main -Dmodel=mnist-1 -Dtarget=native