Energy Considerations for Large Pretrained Neural Networks

2 June 2025

Papers citing "Energy Considerations for Large Pretrained Neural Networks"

15 / 15 papers shown

Title
From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference S. Samsi Dan Zhao Joseph McDonald Baolin Li Adam Michaleas Michael Jones William Bergeron J. Kepner Devesh Tiwari V. Gadepally 53 142 0 04 Oct 2023
On the Steganographic Capacity of Selected Learning Models Rishit Agrawal Kelvin Jou Tanush Obili Daksh Parikh Samarth Prajapati Yash Seth Charan Sridhar Nathan Zhang Mark Stamp 29 1 0 29 Aug 2023
Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks Atli Kosson Bettina Messmer Martin Jaggi 53 17 0 26 May 2023
A ConvNet for the 2020s Zhuang Liu Hanzi Mao Chaozheng Wu Christoph Feichtenhofer Trevor Darrell Saining Xie ViT 159 5,171 0 10 Jan 2022
Language Models are Few-Shot Learners Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan ... Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever Dario Amodei BDL 768 42,055 0 28 May 2020
Quantization Networks Jiwei Yang Xu Shen Jun Xing Xinmei Tian Houqiang Li Bing Deng Jianqiang Huang Xiansheng Hua MQ 72 346 0 21 Nov 2019
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference Qing Qin Jie Ren Jia-Le Yu Ling Gao Hai Wang Jie Zheng Yansong Feng Jianbin Fang Zheng Wang 18 22 0 21 Oct 2018
Benchmark Analysis of Representative Deep Neural Network Architectures Simone Bianco Rémi Cadène Luigi Celona Paolo Napoletano BDL 66 677 0 01 Oct 2018
The Marginal Value of Adaptive Gradient Methods in Machine Learning Ashia Wilson Rebecca Roelofs Mitchell Stern Nathan Srebro Benjamin Recht ODL 65 1,032 0 23 May 2017
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 772 36,813 0 25 Aug 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,020 0 10 Dec 2015
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han Huizi Mao W. Dally 3DGS 257 8,842 0 01 Oct 2015
Going Deeper with Convolutions Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed Dragomir Anguelov D. Erhan Vincent Vanhoucke Andrew Rabinovich 465 43,658 0 17 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.6K 100,386 0 04 Sep 2014
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation Emily L. Denton Wojciech Zaremba Joan Bruna Yann LeCun Rob Fergus FAtt 177 1,689 0 02 Apr 2014