IrEne-viz: Visualizing Energy Consumption of Transformer Models
Yash Kumar Lal, Reetu Singh, Harsh Trivedi, Qingqing Cao, Aruna Balasubramanian, Niranjan Balasubramanian
Abstract
IrEne is an energy prediction system that accurately predicts the interpretable inference energy consumption of a wide range of Transformer-based NLP models. We present the IrEne-viz tool, an online platform for visualizing and exploring energy consumption of various Transformer-based models easily. Additionally, we release a public API that can be used to access granular information about energy consumption of transformer models and their components. The live demo is available at http://stonybrooknlp.github.io/irene/demo/.- Anthology ID:
- 2021.emnlp-demo.29
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Heike Adel, Shuming Shi
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 251–258
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-demo.29
- DOI:
- 10.18653/v1/2021.emnlp-demo.29
- Cite (ACL):
- Yash Kumar Lal, Reetu Singh, Harsh Trivedi, Qingqing Cao, Aruna Balasubramanian, and Niranjan Balasubramanian. 2021. IrEne-viz: Visualizing Energy Consumption of Transformer Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 251–258, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- IrEne-viz: Visualizing Energy Consumption of Transformer Models (Lal et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2021.emnlp-demo.29.pdf