Complexity Reduction of CNNs using Multi-Scale Group Convolution for IoT Edge Sensors
| Autor: | Qingyuan Wang, Antoine Frappe, Benoit Larras, Barry Cardiff, Deepu John |
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| Přispěvatelé: | University College Dublin [Dublin] (UCD), Microélectronique Silicium - IEMN (MICROELEC SI - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), This publication has emanated from research supported in part by 1) a grant from Science Foundation Ireland under Grant number 18/CRT/6183 and 2) CHIST-ERA grant JEDAI CHIST-ERA-18-ACAI-003., ANR-19-CHR3-0005,JEDAI,Event Driven Artificial Intelligence Hardware for Biomedical Sensors(2019) |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS) 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Oct 2022, Glasgow, United Kingdom. pp.1-4, ⟨10.1109/ICECS202256217.2022.9970790⟩ |
| DOI: | 10.1109/icecs202256217.2022.9970790 |
| Popis: | International audience; In this paper, we propose Multi-Scale Group Convolution (MSGC) an optimization to the conventional convolutional layer, to address the high computational complexity issue in deploying convolutional neural networks (CNN) on the Internet of Things (IoT) enabled edge sensors. The proposed method reduces complexity by grouping input channels of a convolution layer into smaller groups, thereby reducing the number of intermediate connections and complexity of matrix computations in a CNN. This approach results in a minor performance loss, which is compensated by utilizing a characteristic of group convolution to extract multi-scale features. The proposed technique is applied for detecting cardiac arrhythmias from electrocardiogram (ECG) data using CNNs to be deployed in edge sensors. For the binary classification of ECG into Normal or Anomalous beats, the proposed MSGC-based CNN achieved an average 30% reduction in computations while achieving similar or better performance compared to the conventional CNNs. We used the Physionet MIT-BIH Arrhythmia database for performance evaluation, and in the best scenario, our approach increases accuracy by 0.47%, F1 score by 1.87% while only using 64.41% MACs and 83.62% parameters. This optimization strategy can be extended to other CNN models where computational complexity reduction is critical for deployment in edge devices. |
| Databáze: | OpenAIRE |
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