# Benchmarking Inference Performance of Deep Learning Models on Analog Devices

arXiv: https://arxiv.org/abs/2011.11840 · HTML: https://mlsanity.com/paper/2011.11840/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Neural Network Compression for Noisy Storage Devices](https://mlsanity.com/paper/2102.07725/) (2021)
- [Otters: An Energy-Efficient SpikingTransformer via Optical Time-to-First-Spike Encoding](https://mlsanity.com/paper/2509.18968/) (2025)
- [Denoising Noisy Neural Networks: A Bayesian Approach with Compensation](https://mlsanity.com/paper/2105.10699/) (2021)
- [Exploring the Impact of Virtualization on the Usability of the Deep Learning Applications](https://mlsanity.com/paper/2112.09780/) (2021)
- [Effect of Batch Normalization on Noise Resistant Property of Deep Learning Models](https://mlsanity.com/paper/2205.07372/) (2022)
- [Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models](https://mlsanity.com/paper/2205.04886/) (2022)
- [Impact of Learning Rate on Noise Resistant Property of Deep Learning Models](https://mlsanity.com/paper/2205.07856/) (2022)

## Abstract (arXiv)

Analog hardware implemented deep learning models are promising for computation and energy constrained systems such as edge computing devices. However, the analog nature of the device and the associated many noise sources will cause changes to the value of the weights in the trained deep learning models deployed on such devices. In this study, systematic evaluation of the inference performance of trained popular deep learning models for image classification deployed on analog devices has been carried out, where additive white Gaussian noise has been added to the weights of the trained models during inference. It is observed that deeper models and models with more redundancy in design such as VGG are more robust to the noise in general. However, the performance is also affected by the design philosophy of the model, the detailed structure of the model, the exact machine learning task, as well as the datasets.