The ZAnt Project
A Zig library for efficient ML model training on embedded systems.
The ZAnt Project A Zig library for efficient ML model training on embedded systems.
Zant is an open-source SDK designed to simplify and cost-effectively deploy machine learning (ML) models on embedded and edge devices. With Zant, developers can easily optimize and deploy models on a wide range of hardware, minimizing the need for complex reimplementation when switching platforms.
The first release of Zant will be a static library that takes an ML model as input and produces an optimized, device-specific executable. Built primarily in the Zig programming language, Zant leverages two powerful features of Zig:
Cross-Compilation: Zant enables seamless code portability, allowing ML models to run on different device architectures with minimal adjustments. This ensures flexibility and saves development time, especially in resource-constrained environments.
C-Compatibility: As C is the standard language for embedded applications, Zant’s compatibility with C allows it to integrate smoothly with essential components like the Hardware Abstraction Layer (HAL), which provides a consistent interface for hardware interactions.
With Zant, deploying ML models to embedded and edge devices becomes more efficient, flexible, and accessible.
To contribute, please visit our GitHub repository and follow the guidelines in the contributing document.
The Tiny Book Project provides efficient machine learning model training for embedded systems with constrained resources.
This library is cross-platform, supporting ARM Cortex-M, RISC-V, and others.
Getting started requires the latest Zig compiler and foundational Zig knowledge.