Efficient Detection of Microplastics on Edge Devices With Tailored Compiler for TinyML Applications
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 algorith...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10990265/ |
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| _version_ | 1850185174976823296 |
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| author | Alessandro Cerioli Lorenzo Petrosino Daniele Sasso Clement Laroche Tobias Piechowiak Luca Pezzarossa Mario Merone Luca Vollero Anna Sabatini |
| author_facet | Alessandro Cerioli Lorenzo Petrosino Daniele Sasso Clement Laroche Tobias Piechowiak Luca Pezzarossa Mario Merone Luca Vollero Anna Sabatini |
| author_sort | Alessandro Cerioli |
| collection | DOAJ |
| description | 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 <monospace>ONNX Runtime</monospace>, 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. |
| format | Article |
| id | doaj-art-4fe983229f3b4944a834b307192c7aee |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4fe983229f3b4944a834b307192c7aee2025-08-20T02:16:49ZengIEEEIEEE Access2169-35362025-01-0113909709098210.1109/ACCESS.2025.356781610990265Efficient Detection of Microplastics on Edge Devices With Tailored Compiler for TinyML ApplicationsAlessandro Cerioli0https://orcid.org/0009-0003-3177-7381Lorenzo Petrosino1https://orcid.org/0000-0002-1572-8075Daniele Sasso2https://orcid.org/0009-0009-3137-9715Clement Laroche3https://orcid.org/0000-0003-0401-8091Tobias Piechowiak4Luca Pezzarossa5https://orcid.org/0000-0002-0863-2526Mario Merone6https://orcid.org/0000-0002-9406-2397Luca Vollero7https://orcid.org/0000-0002-6928-0157Anna Sabatini8https://orcid.org/0000-0002-6206-5366DTU Compute, Technical University of Denmark, Kongens Lyngby, DenmarkDepartment of Engineering, Research Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, ItalyDepartment of Engineering, Research Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, ItalyGN Audio, Ballerup, DenmarkGN Audio, Ballerup, DenmarkDTU Compute, Technical University of Denmark, Kongens Lyngby, DenmarkDepartment of Engineering, Research Unit of Intelligent Health Technologies, Università Campus Bio-Medico di Roma, Rome, ItalyDepartment of Engineering, Research Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, ItalyDepartment of Engineering, Research Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, ItalyThe 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 <monospace>ONNX Runtime</monospace>, 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.https://ieeexplore.ieee.org/document/10990265/Microplastic detectioncompilerTinyMLquantization |
| spellingShingle | Alessandro Cerioli Lorenzo Petrosino Daniele Sasso Clement Laroche Tobias Piechowiak Luca Pezzarossa Mario Merone Luca Vollero Anna Sabatini Efficient Detection of Microplastics on Edge Devices With Tailored Compiler for TinyML Applications IEEE Access Microplastic detection compiler TinyML quantization |
| title | Efficient Detection of Microplastics on Edge Devices With Tailored Compiler for TinyML Applications |
| title_full | Efficient Detection of Microplastics on Edge Devices With Tailored Compiler for TinyML Applications |
| title_fullStr | Efficient Detection of Microplastics on Edge Devices With Tailored Compiler for TinyML Applications |
| title_full_unstemmed | Efficient Detection of Microplastics on Edge Devices With Tailored Compiler for TinyML Applications |
| title_short | Efficient Detection of Microplastics on Edge Devices With Tailored Compiler for TinyML Applications |
| title_sort | efficient detection of microplastics on edge devices with tailored compiler for tinyml applications |
| topic | Microplastic detection compiler TinyML quantization |
| url | https://ieeexplore.ieee.org/document/10990265/ |
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