Beyond Noise: Understanding and Overcoming Temperature Effects in Analog DNN Inference

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.

Machine Learning (cs.LG)

arXiv:2609.15527 [cs.LG]

(or arXiv:2609.15527v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.15527 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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