English(EN) Design-Time Optimization of Deep Neural Networks for Intermittent Learning on Microcontrollers

新方法优化微控制器上深度神经网络的间歇式学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-05 04:00

研究人员开发了一种方法，用于优化微控制器上深度神经网络（DNN）的间歇式学习，特别适用于能量收集系统。该方法结合了硬件感知能量预测模型和多目标优化，以离线选择最优的DNN架构。能量预测器估算每层的推理和训练功耗，并考虑了检查点开销，在Cortex-M4 MCU上进行了验证，平均预测误差为16.6%。这项工作通过连接设计时优化和间歇式学习能力，促进了边缘设备的自主AI。 AI

影响为低功耗、能量受限的边缘设备上更强大、更自主的AI应用提供了支持。

排序理由学术论文，详细介绍了优化微控制器上DNN的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

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Newsworthiness bucket: Tool; 学术论文，详细介绍了优化微控制器上DNN的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Jakob Schubert, Maximilian Kasper, Maximilian Linke, Benedict Herzog, Mark Deutel, Axel Plinge, Dominik Seuss, Christopher Mutschler · 2026-08-05 04:00

面向微控制器间歇学习的深度神经网络设计时优化

arXiv:2608.03589v1 Announce Type: new Abstract: 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, ex…

本文如何被排名

报道来源 [1]

面向微控制器间歇学习的深度神经网络设计时优化

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