Problem
Research questions and friction points this paper is trying to address.
temperature effects
analog DNN inference
inference accuracy
non-idealities
thermal conditions
Beyond Noise: Understanding and Overcoming Temperature Effects in Analog DNN Inference
🤖 AI Summary
研究针对模拟DNN推理中的温度效应问题，通过实验分析和比较多种缓解策略，发现系统性非理想因素是主要原因，并提出温度感知校准等方法提高鲁棒性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Temperature Effects
Systematic Non-idealities
Noise-aware Training
Hardware-in-the-loop Training
Temperature-aware Calibration
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