# Tensor-Aware Energy Accounting

arXiv: https://arxiv.org/abs/2311.11424 · HTML: https://mlsanity.com/paper/2311.11424/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 69 references that are in the map.
- First posted on arXiv 19 Nov 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Tensorizing Neural Networks](https://mlsanity.com/paper/1509.06569/) (2015)
- [Explaining NonLinear Classification Decisions with Deep Taylor Decomposition](https://mlsanity.com/paper/1512.02479/) (2015)
- [Compressing Deep Convolutional Networks using Vector Quantization](https://mlsanity.com/paper/1412.6115/) (2014)
- [Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training](https://mlsanity.com/paper/2208.06102/) (2022)
- [SqueezeNext: Hardware-Aware Neural Network Design](https://mlsanity.com/paper/1803.10615/) (2018)
- [Energy Usage Reports: Environmental awareness as part of algorithmic accountability](https://mlsanity.com/paper/1911.08354/) (2019)
- [DeepLocalize: Fault Localization for Deep Neural Networks](https://mlsanity.com/paper/2103.03376/) (2021)
- [NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks](https://mlsanity.com/paper/1710.05420/) (2017)
- [DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs](https://mlsanity.com/paper/2205.01938/) (2022)
- [Dynamic GPU Energy Optimization for Machine Learning Training Workloads](https://mlsanity.com/paper/2201.01684/) (2022)
- [Neural Network Module Decomposition and Recomposition](https://mlsanity.com/paper/2112.13208/) (2021)

## Built on by (most cited first, 1 of 1)

- [Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices](https://mlsanity.com/paper/2506.01774/) (2025)

## Abstract (arXiv)

With the rapid growth of Artificial Intelligence (AI) applications supported by deep learning (DL), the energy efficiency of these applications has an increasingly large impact on sustainability. We introduce Smaragdine, a new energy accounting system for tensor-based DL programs implemented with TensorFlow. At the heart of Smaragdine is a novel white-box methodology of energy accounting: Smaragdine is aware of the internal structure of the DL program, which we call tensor-aware energy accounting. With Smaragdine, the energy consumption of a DL program can be broken down into units aligned with its logical hierarchical decomposition structure. We apply Smaragdine for understanding the energy behavior of BERT, one of the most widely used language models. Layer-by-layer and tensor-by-tensor, Smaragdine is capable of identifying the highest energy/power-consuming components of BERT. Furthermore, we conduct two case studies on how Smaragdine supports downstream toolchain building, one on the comparative energy impact of hyperparameter tuning of BERT, the other on the energy behavior evolution when BERT evolves to its next generation, ALBERT.