MicroFlow: An Efficient Rust-Based Inference Engine for TinyML
This addresses the need for robust and efficient machine learning deployment on tiny, bare-metal devices in IoT, robotics, and industrial applications, representing an incremental improvement over existing frameworks.
The authors tackled the problem of deploying neural networks on resource-constrained embedded systems by developing MicroFlow, an efficient Rust-based inference engine for TinyML, which successfully runs on devices with only 2kB of RAM and uses less memory than other state-of-the-art solutions while achieving equally accurate but faster inference on medium-size models.
In recent years, there has been a significant interest in developing machine learning algorithms on embedded systems. This is particularly relevant for bare metal devices in Internet of Things, Robotics, and Industrial applications that face limited memory, processing power, and storage, and which require extreme robustness. To address these constraints, we present MicroFlow, an open-source TinyML framework for the deployment of Neural Networks (NNs) on embedded systems using the Rust programming language. The compiler-based inference engine of MicroFlow, coupled with Rust's memory safety, makes it suitable for TinyML applications in critical environments. The proposed framework 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), achieving equally accurate but 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.