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

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

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 temp

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