1 code implementation • EMNLP (ACL) 2021 • Yash Kumar Lal, Reetu Singh, Harsh Trivedi, Qingqing Cao, Aruna Balasubramanian, Niranjan Balasubramanian
IrEne is an energy prediction system that accurately predicts the interpretable inference energy consumption of a wide range of Transformer-based NLP models.
1 code implementation • ACL 2021 • Qingqing Cao, Yash Kumar Lal, Harsh Trivedi, Aruna Balasubramanian, Niranjan Balasubramanian
We present IrEne, an interpretable and extensible energy prediction system that accurately predicts the inference energy consumption of a wide range of Transformer-based NLP models.
no code implementations • 10 Dec 2020 • Qingqing Cao, Oriana Riva, Aruna Balasubramanian, Niranjan Balasubramanian
We present a practical approach, called BewQA, that can answer Bew queries by mining a template of the business-related webpages and using the template to guide the search.
1 code implementation • EMNLP (sustainlp) 2020 • Qingqing Cao, Aruna Balasubramanian, Niranjan Balasubramanian
In this work, we show that existing software-based energy measurements are not accurate because they do not take into account hardware differences and how resource utilization affects energy consumption.
1 code implementation • ACL 2020 • Qingqing Cao, Harsh Trivedi, Aruna Balasubramanian, Niranjan Balasubramanian
It turns out that we can get by without input-wide self-attention at all layers, especially in the lower layers.
1 code implementation • 3 Jun 2017 • Qingqing Cao, Niranjan Balasubramanian, Aruna Balasubramanian
In this paper, we explore optimizations to run Recurrent Neural Network (RNN) models locally on mobile devices.