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Efficient Detection of Microplastics on Edge Devices With Tailored Compiler for TinyML Applications
Summary
Two AI models (MLP and GRU) were designed for microplastic detection in water from scattered optical signals and optimized via neural architecture search, then compiled with a custom TinyML compiler for deployment on resource-constrained edge devices—enabling low-power, distributed microplastic monitoring.
The current study aims to train and benchmark AI models tailored for the detection of microplastic in water from scattered signals. We trained two different models, the first based on a Multi-Layer Perceptron (MLP) and the second on a Gated Recurrent Unit (GRU). A Neural Architecture Search algorithm was used to determine the optimal configuration for each of the two models. Moreover, for deployment on edge devices, a specific custom-made compiler was designed and used. The compiler is specifically designed for TinyML applications and, therefore, for resource-constrained devices. It bypasses traditional inference engines, compiling the NNs to native C code using only standard C libraries. Our approach demonstrated better performance compared to state-of-the-art frameworks such as ONNX Runtime, achieving better latency, memory usage, energy consumption, and a higher portability. This highlights the potential of our method for efficient and effective microplastic detection in environmental monitoring.