AI 中文总结
本研究提出结合硬件感知能量预测模型与多目标优化的方法，在设计阶段离线优化微控制器上的DNN，通过Cortex-M4 MCU验证了方法有效性，实现边缘端自主AI。
AI 中文摘要
我们提出一种为微控制器单元（MCU）设计深度神经网络（DNN）的方法，用于能量自主型设备上的间歇式学习。在移动应用中，能量可能耗尽，例如太阳能供电时，执行人工智能（AI）会面临技术问题，因为学习过程可能随时被中断。我们的方法结合了硬件感知能量预测模型与多目标优化（MOO），可在设计阶段离线优化DNN，无需在目标MCU上反复部署和在线测试。所提出的能量预测器基于从DNN模型提取的特定于实现的计算和内存特征，估算DNN推理与训练的每层能耗，包括间歇式检查点开销。我们在Cortex-M4 MCU上使用用于异常检测的自编码器验证了该方法，预测器的加权绝对百分比误差为16.6%，足以在间歇式约束下进行可靠的架构选择。因此，本研究弥合了MOO、自动化DNN设计、能量收集系统部署与间歇式学习之间的差距，真正实现了边缘端自主AI。
英文摘要
We present a method for designing deep neural networks (DNNs) for intermittent, energy-autonomous, on-device learning on microcontroller units (MCUs). In mobile applications where the energy can run out, e.g., when solar-powered, executing artificial intelligence (AI) faces a technical issue as learning can be interrupted at any time. Our approach combines a hardware-aware energy prediction model with multi-objective optimization (MOO), enabling offline DNN optimization at the design stage without repeated deployment and online testing on the target MCU. Our proposed energy predictor estimates per-layer energy consumption for both DNN inference and training, including the intermittent checkpointing overhead, based on implementation-specific compute and memory features extracted from the DNN model. We validate our approach using autoencoders for anomaly detection on a Cortex-M4 MCU, where our predictor achieves a weighted absolute percentage error of 16.6%, which is sufficient for reliable architecture selection under intermittency constraints. As a result, this work bridges the gap between MOO, automated DNN design, deployment on energy-harvesting systems, and intermittent learning, truly enabling autonomous AI at the edge.
CommentsAccepted at the 7th Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM) collocated with ECML PKDD 2026, 12 pages, 5 figures, 1 table,
