Z-Ant

Beer model timing (QEMU, Cortex-M55):

• Reference: 880.58 ms
• CMSIS-NN: 828.84 ms
• Improvement: 51.74 ms (5.9%)

🛠️ CI/CD

• zig-tests – regression suite covering the core runtime.
• zig-codegen-tests – validates generated operators and glue code.
• zant-benchmarks – runs the Beer end-to-end benchmark and refreshes the metrics below.

📈 Performance Snapshot

Project Overview

✨ Why Z-Ant?

• ⚡ Microsecond inference on ARM Cortex-M, RISC-V, x86
• 📦 Zero dependencies - single static library deployment
• 🎯 ONNX native - direct model deployment from ONNX
• 🔧 30+ operators - comprehensive neural network support
• 📷 Built-in image processing - JPEG decode + preprocessing
• 🧠 Smart optimization - quantization, pruning, memory efficiency

Use Cases

• 🏭 Edge AI: Real-time anomaly detection, predictive maintenance
• 🤖 IoT & Autonomous Systems: Lightweight AI models for drones, robots, vehicles, IoT devices
• 📱 Mobile Applications: On-device inference for privacy-preserving AI
• 🏥 Medical Devices: Real-time health monitoring and diagnostics
• 🎮 Gaming: AI-powered gameplay enhancement on embedded systems

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🚀 Quick Start

Prerequisites

• Zig 0.14.x (run ./scripts/install_zig.sh to fetch a local copy; set ZIG_DOWNLOAD_URL=file:///absolute/path/to/zig-linux-x86_64-0.14.0.tar.xz when working from a pre-downloaded archive)
• qemu-system-arm 7.2+ (install via ./scripts/install_qemu.sh or your platform package manager when running the STM32 N6 QEMU harness)

Get Started in 2 Minutes

# Clone and verify installation
git clone https://github.com/ZantFoundation/Z-Ant.git
cd Z-Ant

# Install Zig 0.14 locally (optional if you already have it)
./scripts/install_zig.sh
# (use ZIG_DOWNLOAD_URL or ZIG_DOWNLOAD_BASE to point at a mirror if needed)
export PATH="$(pwd)/.zig-toolchain/current:$PATH"

# - put your onnx model inside /datasets/models in a folder with the same of the model to to have: /datasets/models/my_model/my_model.onnx

# - simplify and prepare the model for zant inference engine
./zant input_setter --model my_model --shape your,model,sha,pe

# - Generate test data
./zant user_tests_gen --model my_model

# --- GENERATING THE Single Node lib and test it ---
#For a N nodes model it creates N onnx models, one for each node with respective tests.
./zant onnx_extract  --model my_model

#generate libs for extracted nodes
zig build extractor-gen -Dmodel="my_model"

#test extracted nodes
zig build extractor-test -Dmodel="my_model" 

# --- GENERATING THE LIBRARY and TESTS ---
# Generate code for a specific model
zig build lib-gen -Dmodel="my_model" -Denable_user_tests [ -Ddo_export -Dlog -Dcomm ... ]

# Test the generated code
zig build lib-test -Dmodel="my_model" -Denable_user_tests [ -Ddo_export -Dlog -Dcomm ... ]

# Build the static library
zig build lib -Dmodel="my_model" [-Doptimize=Release? -Dtarget=... -Dcpu=...]

📖 Essential Commands

IMPORTANT: see ZANT CLI for a better understanding and more details!

Core Workflow

Command	What it does
zig build test	Verify everything works
zig build codegen -Dmodel=<name>	Generate code from ONNX model
zig build lib -Dmodel=<name>	Build deployable static library
zig build test-generated-lib -Dmodel=<name>	Test your generated code

Target Platforms

Platform	Target Flag	CPU Examples
ARM Cortex-M	-Dtarget=thumb-freestanding	-Dcpu=cortex_m33, -Dcpu=cortex_m4, -Dcpu=cortex_m55
RISC-V	-Dtarget=riscv32-freestanding	-Dcpu=generic_rv32
x86/Native	-Dtarget=native	(auto-detected)

Key Options

Option	Description	Example
-Dmodel=<name>	Your model name	-Dmodel=my_classifier
-Dmodel_path=<path>	Custom ONNX file	-Dmodel_path=models/custom.onnx
-Dlog=true	Enable detailed logging	-Dlog=true
-Dcomm=true	Add comments to generated code	-Dcomm=true
-Dstm32n6_accel=true	Enable STM32 N6 accelerator dispatch layer	-Dstm32n6_accel=true
-Dstm32n6_cmsis_path=/abs/path	Optional CMSIS include root used when the accelerator flag is set	-Dstm32n6_cmsis_path="/opt/CMSIS_6/Source"
-Dstm32n6_use_cmsis=true	Use CMSIS Helium helpers (requires CMSIS-DSP headers or third_party/CMSIS-NN)	zig build test -Dstm32n6_accel=true -Dstm32n6_use_cmsis=true
-Dstm32n6_use_ethos=true	Enable Ethos-U execution path (requires Ethos-U driver headers)	zig build test -Dstm32n6_accel=true -Dstm32n6_use_ethos=true
-Dstm32n6_force_native=true	Force the STM32 N6 accelerator shim to run on the host (useful for smoke tests)	zig build test -Dstm32n6_accel=true -Dstm32n6_force_native=true

Optional SDK downloads

# Fetch CMSIS-NN into third_party/CMSIS-NN
./scripts/fetch_cmsis_nn.sh
# (set CMSIS_NN_REPO or CMSIS_NN_REF to use a mirror/specific release)
# (set CMSIS_NN_ARCHIVE=/absolute/path/to/CMSIS-NN-main.zip to install from a local archive without network access)

# Fetch the Arm Ethos-U core driver
./scripts/fetch_ethos_u_driver.sh
# (set ETHOS_U_REPO / ETHOS_U_REF to use a fork or pinned revision)
# (set ETHOS_U_ARCHIVE=/absolute/path/to/ethos-u-driver.zip for offline installs)

# Install qemu-system-arm for the STM32 N6 QEMU regression harness
./scripts/install_qemu.sh
# (requires administrator privileges; set QEMU_SKIP_APT_UPDATE=1 to skip `apt-get update` on Debian/Ubuntu)

STM32 N6 accelerator testing

# Host smoke test for the C shim (builds reference/CMSIS/Ethos shared objects)
./scripts/test_stm32n6_conv.py

# Bare-metal regression harness executed inside QEMU
# Automatically discovers CMSIS-DSP / CMSIS-NN in third_party; pass --cmsis-include/--cmsis-nn-include to override.
./scripts/test_stm32n6_qemu.py --arm-prefix arm-none-eabi --repeat 3

# Sample output (PASS markers are emitted by the firmware, the harness exits immediately after the first PASS):
#   [run]   reference
#   stm32n6 reference PASS
#   ✅ reference completed in 40.57 ms

The script terminates QEMU as soon as the PASS banner appears, so the reported time reflects the actual firmware runtime instead of the former 3 s watchdog timeout. A non-zero exit status accompanied by fatal: unexpected exception indicates a crash inside the firmware before the PASS message is printed.

💼 Integration Examples

CMake Integration

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

Arduino/Embedded C

#include "lib_my_model.h"

// Optional: Set custom logging
extern void setLogFunction(void (*log_function)(uint8_t *string));

// Your inference code here

🎯 Real-World Examples

Image Classification on Cortex-M33

# Generate optimized library for image classifier
zig build codegen -Dmodel=mobilenet_v2 -Dmodel_path=models/mobilenet.onnx
zig build lib -Dmodel=mobilenet_v2 -Dtarget=thumb-freestanding -Dcpu=cortex_m33 -Doutput_path=deployment/

Multi-Platform Testing

# Test on different architectures
zig build test-generated-lib -Dmodel=my_model -Dtarget=native
zig build test-generated-lib -Dmodel=my_model -Dtarget=thumb-freestanding -Dcpu=cortex_m4

🛠️ Development

For Contributors

# Run full test suite
zig build test --summary all

# Test heavy computational operations  
zig build test -Dheavy=true

# Test specific operator implementations
zig build op-codegen-test -Dop=Conv

# Generate and test single operations
zig build op-codegen-gen -Dop=Add

Project Structure

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

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🤝 Contributing

We welcome contributions from developers of all skill levels! Here's how to get involved:

Getting Started

1. Fork the repository on GitHub
2. Clone your fork locally
3. Create a feature branch for your work
4. Make your changes following our coding standards
5. Run tests to ensure everything works
6. Submit a pull request for review

Ways to Contribute

• 🐛 Bug Reports: Found an issue? Let us know!
• ✨ Feature Requests: Have an idea? Share it with us!
• 💻 Code Contributions: Improve the codebase or add new features
• 📚 Documentation: Help make the project easier to understand
• 🧪 Testing: Write tests or improve test coverage

Community Guidelines

• Follow our Code of Conduct
• Check out the Contributing Guide for detailed guidelines
• Join discussions on GitHub Issues and Discussions

Recognition

All contributors are recognized in our Contributors list. Thank you for helping shape the future of tinyML!

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📄 License

This project is licensed under the LICENSE file in the repository.

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Join us in revolutionizing AI on edge devices! 🚀

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