Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers

• URL: http://arxiv.org/abs/2506.10851v1
• Date: Thu, 12 Jun 2025 16:10:22 GMT
• Title: Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers
• Authors: Adel Chehade, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino,
• Abstract summary: We present a practical deep learning (DL) approach for energy-efficient traffic classification on resource-limited microcontrollers.<n>We develop a lightweight 1D-CNN, optimized via hardware-aware neural architecture search (HW-NAS), which achieves 96.59% accuracy on the ISCX VPN-Non-VPN dataset.<n>We evaluate real-world inference performance on two microcontrollers.
• Score: 1.3124513975412255
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we present a practical deep learning (DL) approach for energy-efficient traffic classification (TC) on resource-limited microcontrollers, which are widely used in IoT-based smart systems and communication networks. Our objective is to balance accuracy, computational efficiency, and real-world deployability. To that end, we develop a lightweight 1D-CNN, optimized via hardware-aware neural architecture search (HW-NAS), which achieves 96.59% accuracy on the ISCX VPN-NonVPN dataset with only 88.26K parameters, a 20.12K maximum tensor size, and 10.08M floating-point operations (FLOPs). Moreover, it generalizes across various TC tasks, with accuracies ranging from 94% to 99%. To enable deployment, the model is quantized to INT8, suffering only a marginal 1-2% accuracy drop relative to its Float32 counterpart. We evaluate real-world inference performance on two microcontrollers: the high-performance STM32F746G-DISCO and the cost-sensitive Nucleo-F401RE. The deployed model achieves inference latencies of 31.43ms and 115.40ms, with energy consumption of 7.86 mJ and 29.10 mJ per inference, respectively. These results demonstrate the feasibility of on-device encrypted traffic analysis, paving the way for scalable, low-power IoT security solutions.

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