发表机构
Heidelberg University（海德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过实验探究温度对模拟DNN推理的影响，发现系统性非理想特性是性能下降主因，并提出硬件在环训练和温度感知校准作为最有效的缓解策略。
AI 中文摘要
模拟计算的高能效使其成为在移动和嵌入式设备等资源受限平台上部署计算密集型机器学习工作负载的最有前景的候选方案之一。然而，模拟加速器本质上容易受到由物理组件变化引起的噪声和非理想特性的影响，而这些行为进一步对环境因素敏感。这些效应可能显著降低推理精度。在本工作中，我们对一个具有代表性的模拟硬件实例进行了全面的实验研究，以探究温度的影响。我们首先刻画了在一系列工作温度下随机性和系统性非理想特性的行为。随后，我们比较了一组基于模拟和基于硬件的缓解策略，旨在提高对温度引起的性能下降的鲁棒性。我们的结果表明，温度引起的性能下降主要由系统性非理想特性驱动，而非仅由随机噪声导致。噪声感知训练提高了鲁棒性，而硬件在环训练和温度感知校准在变化的热条件下提供了最强的精度保持能力。
英文摘要
The energy efficiency of analog computing makes it one of the most promising candidates for deploying resource-intensive machine learning workloads on constrained platforms such as mobile and embedded devices. However, analog accelerators are inherently susceptible to noise and non-idealities arising from physical component variations, whose behavior is further sensitive to environmental factors. These effects can significantly degrade inference accuracy. In this work, we conduct a comprehensive experimental study on a representative example of analog hardware to investigate the impact of temperature. We first characterize the behavior of stochastic and systematic non-idealities across a range of operating temperatures. Following this, we compare a set of simulation-based and hardware-based mitigation strategies aimed at improving robustness against temperature-induced performance degradation. Our results suggest that temperature-induced degradation is driven primarily by systematic non-idealities rather than stochastic noise alone. Noise-aware training improves robustness, while hardware-in-the-loop training and temperature-aware calibration provide the strongest accuracy retention across varying thermal conditions.
CommentsPublished at the ECML PKDD Conference 2026, at the 7th Workshop on IoT, Edge, and Mobile for Embedded Machine Learning
