# Double-Exponential Increases in Inference Energy: The Cost of the Race for Accuracy

arXiv: https://arxiv.org/abs/2412.09731 · HTML: https://mlsanity.com/paper/2412.09731/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [Are we done with ImageNet?](https://mlsanity.com/paper/2006.07159/) (2020)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [An Analysis of Deep Neural Network Models for Practical Applications](https://mlsanity.com/paper/1605.07678/) (2016)
- [Part-time Power Measurements: nvidia-smi's Lack of Attention](https://mlsanity.com/paper/2312.02741/) (2023)

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

- [How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference](https://mlsanity.com/paper/2505.09598/) (2025)
- [Energy-Conscious LLM Decoding: Impact of Text Generation Strategies on GPU Energy Consumption](https://mlsanity.com/paper/2502.11723/) (2025)
- [BitLogic: Training Framework for Gradient-Based FPGA-Native Neural Networks](https://mlsanity.com/paper/2602.07400/) (2026)
- [Hardware optimization on Android for inference of AI models](https://mlsanity.com/paper/2511.13453/) (2025)
- [CascadedViT: Cascaded Chunk-FeedForward and Cascaded Group Attention Vision Transformer](https://mlsanity.com/paper/2511.14111/) (2025)
- [Beyond Independent Optimization: Compression, MoE Routing, and Quantization Interactions in Multimodal Edge Intelligence](https://mlsanity.com/paper/2607.20981/) (2026)

## Abstract (arXiv)

Deep learning models in computer vision have achieved significant success but pose increasing concerns about energy consumption and sustainability. Despite these concerns, there is a lack of comprehensive understanding of their energy efficiency during inference. In this study, we conduct a comprehensive analysis of the inference energy consumption of 1,200 ImageNet classification models - the largest evaluation of its kind to date. Our findings reveal a steep diminishing return in accuracy gains relative to the increase in energy usage, highlighting sustainability concerns in the pursuit of marginal improvements. We identify key factors contributing to energy consumption and demonstrate methods to improve energy efficiency. To promote more sustainable AI practices, we introduce an energy efficiency scoring system and develop an interactive web application that allows users to compare models based on accuracy and energy consumption. By providing extensive empirical data and practical tools, we aim to facilitate informed decision-making and encourage collaborative efforts in developing energy-efficient AI technologies.