在物联网设备上运行模型推理，可比传原始图像省电五倍。
Send Less, Save More: Energy-Efficiency Benchmark of Embedded CNN Inference vs. Data Transmission in IoT
- 在ESP32-S3上部署压缩CNN，在设备端完成推理
- 相比传输原始图像，整体能耗降低至1/5
- 适合长期野外监测的低碳智能设备设计
物联网与人工智能的融合为监测生态变化提供了重要机遇。针对远程环境监测中图像数据带来的能源挑战，本文在ESP32-S3平台上评估了低功耗广域网与领域特定数据集训练的压缩卷积神经网络（CNN）的应用效果。实验表明，将推理过程移至设备端，仅传输结果而非原始图像，可使整体能耗降低高达五倍。该研究推动了嵌入式机器学习在低碳、自维持环境监测系统中的应用。
原文摘要 · Abstract (English)
The integration of the Internet of Things (IoT) and Artificial Intelligence offers significant opportunities to enhance our ability to monitor and address ecological changes. As environmental challenges become increasingly pressing, the need for effective remote monitoring solutions is more critical than ever. A major challenge in designing IoT applications for environmental monitoring - particularly those involving image data - is to create energy-efficient IoT devices capable of long-term operation in remote areas with limited power availability. Advancements in the field of Tiny Machine Learning allow the use of Convolutional Neural Networks (CNNs) on resource-constrained, battery-operated microcontrollers. Since data transfer is energy-intensive, performing inference directly on microcontrollers to reduce the message size can extend the operational lifespan of IoT nodes. This work evaluates the use of common Low Power Wide Area Networks and compressed CNNs trained on domain specific datasets on an ESP32-S3. Our experiments demonstrate, among other things, that executing CNN inference on-device and transmitting only the results reduces the overall energy consumption by a factor of up to five compared to sending raw image data. These findings advocate the development of IoT applications with reduced carbon footprint and capable of operating autonomously in environmental monitoring scenarios by incorporating EmbeddedML.
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