TinyAirNet: TinyML Model Transmission for Energy-efficient Image Retrieval from IoT Devices
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866916289074692096 |
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| author | Shiraishi, Junya Thorsager, Mathias Pandey, Shashi Raj Popovski, Petar |
| author_facet | Shiraishi, Junya Thorsager, Mathias Pandey, Shashi Raj Popovski, Petar |
| contents | This letter introduces an energy-efficient pull-based data collection framework for Internet of Things (IoT) devices that use Tiny Machine Learning (TinyML) to interpret data queries. A TinyML model is transmitted from the edge server to the IoT devices. The devices employ the model to facilitate the subsequent semantic queries. This reduces the transmission of irrelevant data, but receiving the ML model and its processing at the IoT devices consume additional energy. We consider the specific instance of image retrieval in a single device scenario and investigate the gain brought by the proposed scheme in terms of energy efficiency and retrieval accuracy, while considering the cost of computation and communication, as well as memory constraints. Numerical evaluation shows that, compared to a baseline scheme, the proposed scheme reaches up to 67% energy reduction under the accuracy constraint when many images are stored. Although focused on image retrieval, our analysis is indicative of a broader set of communication scenarios in which the preemptive transmission of an ML model can increase communication efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_04788 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | TinyAirNet: TinyML Model Transmission for Energy-efficient Image Retrieval from IoT Devices Shiraishi, Junya Thorsager, Mathias Pandey, Shashi Raj Popovski, Petar Networking and Internet Architecture Signal Processing This letter introduces an energy-efficient pull-based data collection framework for Internet of Things (IoT) devices that use Tiny Machine Learning (TinyML) to interpret data queries. A TinyML model is transmitted from the edge server to the IoT devices. The devices employ the model to facilitate the subsequent semantic queries. This reduces the transmission of irrelevant data, but receiving the ML model and its processing at the IoT devices consume additional energy. We consider the specific instance of image retrieval in a single device scenario and investigate the gain brought by the proposed scheme in terms of energy efficiency and retrieval accuracy, while considering the cost of computation and communication, as well as memory constraints. Numerical evaluation shows that, compared to a baseline scheme, the proposed scheme reaches up to 67% energy reduction under the accuracy constraint when many images are stored. Although focused on image retrieval, our analysis is indicative of a broader set of communication scenarios in which the preemptive transmission of an ML model can increase communication efficiency. |
| title | TinyAirNet: TinyML Model Transmission for Energy-efficient Image Retrieval from IoT Devices |
| topic | Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2311.04788 |