# Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement

arXiv: https://arxiv.org/abs/2308.12264 · HTML: https://mlsanity.com/paper/2308.12264/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 97 references that are in the map.
- First posted on arXiv 23 Aug 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning](https://mlsanity.com/paper/1611.05128/) (2016)
- [Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2005.00935/) (2020)
- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks](https://mlsanity.com/paper/1710.05420/) (2017)
- [pCAMP: Performance Comparison of Machine Learning Packages on the Edges](https://mlsanity.com/paper/1906.01878/) (2019)
- [Optimizing performance per watt on GPUs in High Performance Computing: temperature, frequency and voltage effects](https://mlsanity.com/paper/1407.8116/) (2014)
- [On the Energy Consumption of Different Dataframe Processing Libraries -- An Exploratory Study](https://mlsanity.com/paper/2209.05258/) (2022)

## Built on by (most cited first, 5 of 5)

- [Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices](https://mlsanity.com/paper/2506.01774/) (2025)
- [Characterizing Performance-Energy Trade-offs of Large Language Models in Multi-Request Workflows](https://mlsanity.com/paper/2604.09611/) (2026)
- [Dissecting Transformers: A CLEAR Perspective towards Green AI](https://mlsanity.com/paper/2510.02810/) (2025)
- [PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems](https://mlsanity.com/paper/2602.18900/) (2026)
- [Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning](https://mlsanity.com/paper/2607.04577/) (2026)

## Abstract (arXiv)

With the increasing usage, scale, and complexity of Deep Learning (DL) models, their rapidly growing energy consumption has become a critical concern. Promoting green development and energy awareness at different granularities is the need of the hour to limit carbon emissions of DL systems. However, the lack of standard and repeatable tools to accurately measure and optimize energy consumption at a fine granularity (e.g., at method level) hinders progress in this area. This paper introduces FECoM (Fine-grained Energy Consumption Meter), a framework for fine-grained DL energy consumption measurement. FECoM enables researchers and developers to profile DL APIs from energy perspective. FECoM addresses the challenges of measuring energy consumption at fine-grained level by using static instrumentation and considering various factors, including computational load and temperature stability. We assess FECoM's capability to measure fine-grained energy consumption for one of the most popular open-source DL frameworks, namely TensorFlow. Using FECoM, we also investigate the impact of parameter size and execution time on energy consumption, enriching our understanding of TensorFlow APIs' energy profiles. Furthermore, we elaborate on the considerations, issues, and challenges that one needs to consider while designing and implementing a fine-grained energy consumption measurement tool. This work will facilitate further advances in DL energy measurement and the development of energy-aware practices for DL systems.