TY - GEN
T1 - IrEne-viz
T2 - 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2021
AU - Lal, Yash Kumar
AU - Cao, Qingqing
AU - Trivedi, Harsh
AU - Singh, Reetu
AU - Balasubramanian, Aruna
AU - Balasubramanian, Niranjan
N1 - Publisher Copyright:
© 2021 Association for Computational Linguistics.
PY - 2021
Y1 - 2021
N2 - IrEne (Cao et al., 2021) 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/.
AB - IrEne (Cao et al., 2021) 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/.
UR - https://www.scopus.com/pages/publications/85127230823
U2 - 10.18653/v1/2021.emnlp-demo.29
DO - 10.18653/v1/2021.emnlp-demo.29
M3 - Conference contribution
AN - SCOPUS:85127230823
T3 - EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
SP - 251
EP - 258
BT - EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing
PB - Association for Computational Linguistics (ACL)
Y2 - 7 November 2021 through 11 November 2021
ER -