MicroFlow: An Efficient Rust-Based Inference Engine for TinyML
<|reference_start|>MicroFlow: An Efficient Rust-Based Inference Engine for TinyML: 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.<|reference_end|>
@article{carnelos2024microflow:,
    title={MicroFlow: An Efficient Rust-Based Inference Engine for TinyML},
    author={Matteo Carnelos, Francesco Pasti, Nicola Bellotto},
    journal={arXiv preprint arXiv:2409.19432},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19432},
    primaryClass={cs.LG cs.AI}
}