Search Results for author:
Found 6 papers, 5 papers with code
IrEne-viz: Visualizing Energy Consumption of Transformer Models
IrEne is an energy prediction system that accurately predicts the interpretable inference energy consumption of a wide range of Transformer-based NLP models.
IrEne: Interpretable Energy Prediction for Transformers
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.
Bew: Towards Answering Business-Entity-Related Web Questions
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.
Towards Accurate and Reliable Energy Measurement of NLP Models
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.
DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering
It turns out that we can get by without input-wide self-attention at all layers, especially in the lower layers.
MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU
In this paper, we explore optimizations to run Recurrent Neural Network (RNN) models locally on mobile devices.
