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

arXiv: https://arxiv.org/abs/2609.15527 · HTML: https://mlsanity.com/paper/2609.15527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 14 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [Resource-Efficient Neural Networks for Embedded Systems](https://mlsanity.com/paper/2001.03048/) (2020)
- [Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation](https://mlsanity.com/paper/2001.04974/) (2020)
- [Accelerated Analog Neuromorphic Computing](https://mlsanity.com/paper/2003.11996/) (2020)
- [Inference with Artificial Neural Networks on Analog Neuromorphic Hardware](https://mlsanity.com/paper/2006.13177/) (2020)
- [Uncertainty Reasoning with Photonic Bayesian Machines](https://mlsanity.com/paper/2512.02217/) (2025)
- [Variance-Aware Noisy Training: Hardening DNNs against Unstable Analog Computations](https://mlsanity.com/paper/2503.16183/) (2025)
- [Demonstrating Analog Inference on the BrainScaleS-2 Mobile System](https://mlsanity.com/paper/2103.15960/) (2021)
- [On Hardening DNNs against Noisy Computations](https://mlsanity.com/paper/2501.14531/) (2025)

## Abstract (arXiv)

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.