提出通用方法精准估算设备端训练能耗，提升调度效率与续航。
THOR: A Generic Energy Estimation Approach for On-Device Training
- 按层分解模型，利用高斯过程学习每层能耗特征。
- 在多平台实验中将误差降低最高达30%。
- 可指导节能剪枝，实测能耗减少50%，适合边缘AI部署者。
电池供电的移动设备（如智能手机、AR/VR眼镜及各类物联网设备）因计算能力提升和实时数据获取便利，正越来越多用于人工智能训练。然而，设备端训练能耗极高，准确估算能耗对任务调度与可持续性至关重要。现有方法受设备异构性和模型复杂性影响，精度与泛化能力受限。本文提出THOR，一种面向深度神经网络（DNN）训练的通用能耗估算方法。首先分析了DNN的分层能耗可加性特性，将模型细分为各层以实现精细化能耗建模；随后采用高斯过程（GP）拟合层级能耗测量数据，基于可加性原理推算整体能耗。我们在多种真实平台与模型上进行大量实验，结果表明THOR将平均绝对百分比误差（MAPE）最高降低了30%。此外，该方法成功应用于能量感知剪枝，使能耗减少50%，充分验证其通用性与应用潜力。
原文摘要 · Abstract (English)
Battery-powered mobile devices (e.g., smartphones, AR/VR glasses, and various IoT devices) are increasingly being used for AI training due to their growing computational power and easy access to valuable, diverse, and real-time data. On-device training is highly energy-intensive, making accurate energy consumption estimation crucial for effective job scheduling and sustainable AI. However, the heterogeneity of devices and the complexity of models challenge the accuracy and generalizability of existing estimation methods. This paper proposes THOR, a generic approach for energy consumption estimation in deep neural network (DNN) training. First, we examine the layer-wise energy additivity property of DNNs and strategically partition the entire model into layers for fine-grained energy consumption profiling. Then, we fit Gaussian Process (GP) models to learn from layer-wise energy consumption measurements and estimate a DNN's overall energy consumption based on its layer-wise energy additivity property. We conduct extensive experiments with various types of models across different real-world platforms. The results demonstrate that THOR has effectively reduced the Mean Absolute Percentage Error (MAPE) by up to 30%. Moreover, THOR is applied in guiding energy-aware pruning, successfully reducing energy consumption by 50%, thereby further demonstrating its generality and potential.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。