# Operating critical machine learning models in resource constrained regimes

arXiv: https://arxiv.org/abs/2303.10181 · HTML: https://mlsanity.com/paper/2303.10181/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [A White Paper on Neural Network Quantization](https://mlsanity.com/paper/2106.08295/) (2021)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [8-bit Optimizers via Block-wise Quantization](https://mlsanity.com/paper/2110.02861/) (2021)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022)
- [Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities](https://mlsanity.com/paper/2210.06640/) (2022)
- [Towards continual learning in medical imaging](https://mlsanity.com/paper/1811.02496/) (2018)
- [Backprop with Approximate Activations for Memory-efficient Network Training](https://mlsanity.com/paper/1901.07988/) (2019)
- [Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis](https://mlsanity.com/paper/2203.02202/) (2022)

## Built on by (most cited first, 6 of 6)

- [HyperSense: Hyperdimensional Intelligent Sensing for Energy-Efficient Sparse Data Processing](https://mlsanity.com/paper/2401.10267/) (2024)
- [PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/2403.12562/) (2024)
- [Is Adversarial Training with Compressed Datasets Effective?](https://mlsanity.com/paper/2402.05675/) (2024)
- [EBA-AI: Ethics-Guided Bias-Aware AI for Efficient Underwater Image Enhancement and Coral Reef Monitoring](https://mlsanity.com/paper/2507.15036/) (2025)
- [DuPLUS: Dual-Prompt Vision-Language Framework for Universal Medical Image Segmentation and Prognosis](https://mlsanity.com/paper/2510.03483/) (2025)
- [FairQuant: Fairness-Aware Mixed-Precision Quantization for Medical Image Classification](https://mlsanity.com/paper/2602.23192/) (2026)

## Abstract (arXiv)

The accelerated development of machine learning methods, primarily deep learning, are causal to the recent breakthroughs in medical image analysis and computer aided intervention. The resource consumption of deep learning models in terms of amount of training data, compute and energy costs are known to be massive. These large resource costs can be barriers in deploying these models in clinics, globally. To address this, there are cogent efforts within the machine learning community to introduce notions of resource efficiency. For instance, using quantisation to alleviate memory consumption. While most of these methods are shown to reduce the resource utilisation, they could come at a cost in performance. In this work, we probe into the trade-off between resource consumption and performance, specifically, when dealing with models that are used in critical settings such as in clinics.