MicroFlow is an open-source TinyML framework for the deployment of Neural Networks (NNs) on embedded systems using the Rust programming language, specifically designed for efficiency and robustness, which is suitable for applications in critical environments. To achieve these objectives, MicroFlow employs a compiler-based inference engine approach, coupled with Rust's memory safety and features. The proposed solution enables the successful deployment of NNs on highly resource-constrained devices, including bare-metal 8-bit microcontrollers with only 2kB of RAM. Furthermore, MicroFlow is able to use less Flash and RAM memory than other state-of-the-art solutions for deploying NN reference models (i.e. wake-word and person detection). It can also achieve faster inference compared to existing engines on medium-size NNs, and similar performance on bigger ones. The experimental results prove the efficiency and suitability of MicroFlow for the deployment of TinyML models in critical environments where resources are particularly limited.

本研究针对嵌入式系统中神经网络的高效与稳健性缺乏的现状，提出了一个名为MicroFlow的开源TinyML框架。通过基于编译器的推理引擎和Rust语言的内存安全特性，MicroFlow实现了在资源极为受限的设备上有效部署神经网络，并在内存使用和推理速度上优于现有解决方案，在要求严格的应用环境中展现出良好的适应性。

MicroFlow：一个高效的基于Rust的TinyML推理引擎