Z-Ant

- Code generation now supports 29 operations, including: GEMM (General Matrix Multiplication), Convolutions (Conv2D), Activation functions like ReLU, Sigmoid, Leaky ReLU, and more - Over 150 tests ensuring correctness and robustness across different hardware targets - A fuzzing system helps detect mathematical errors and verify the integrity of auto-generated code. Zant supports fully connected networks and simple convolutional architectures, making it suitable for various real-world TinyML applications. Supported Hardware: Zant has already been tested on multiple embedded platforms, showing promising results in real-world deployment: Raspberry Pi Pico (1 & 2) STM32 G4 and H7 Arduino Giga Seeed Camera More devices are being added as testing expands. Roadmap: Zant is still in early development, but we have ambitious goals for the next versions: Expanding code generation to cover more ML operations. - Quantization support (already in progress) to reduce model size and improve efficiency. - YOLO support for real-time object detection on microcontrollers. - Simplified deployment workflows to make it easier to use Zant across different hardware platforms. - CI/CD pipeline to improve reliability and automate testing. - Community engagement with a Telegram/Discord channel launching soon. Why Zig? Zig provides a modern, safer alternative to C, with better memory safety and performance optimizations. Unlike Python-based ML tools, Zant’s Zig-based approach avoids runtime overhead, making it ideal for low-power embedded devices. How to Get Involved: If you’re interested in TinyML, Edge AI, or embedded development, we’d love your feedback and contributions! No prior experience with Zig or TinyML is required—just a willingness to learn and a passion for the project. GitHub: https://github.com/ZantFoundation/Z-Ant Contributor Form:https://airtable.com/appYbTCd8vgMzJzFL/shrcWtM08l3VhAPM7 What do you think? What would you like to see next?