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The increasing use of information technology has led to a significant share of energy consumption and carbon emissions from data centers.
“The Evolved Transformer”, 2019
David So, Quoc. Le and Chen Liang · 1901
Earlier work this paper cites.
“EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”
Mingxing Tan and Quoc. Le · 1905
Earlier work this paper cites.
“Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning”
Mahmoud Assran et al · 1906
Earlier work this paper cites.
“Quantifying the Carbon Emissions of Machine Learning”
Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt and Thomas Dandres · 1910
Earlier work this paper cites.
“DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter”
Sanh, Debut, Chaumond and Wolf · 1910
Earlier work this paper cites.
“PROW: A Step Toward Automatic Program Writing”
Richard. Waldinger and Richard.. Lee · 1969
Earlier work this paper cites.
“Superoptimizer - A Look at the Smallest Program”
Henry Massalin · 1987
Earlier work this paper cites.
“On the Synthesis of a Reactive Module”
A. Pnueli and R. Rosner · 1989
Earlier work this paper cites.
“Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning”
Peter Henderson et al · 2002
Earlier work this paper cites.
“Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping”
Jesse Dodge et al · 2002
Earlier work this paper cites.
“GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding”
Dmitry Lepikhin et al · 2006
Earlier work this paper cites.
“Dimensions in Program Synthesis”
Sumit Gulwani · 2010
Earlier work this paper cites.
“The World’s Leading Online Programming Learning Platform — leetcode.com” [Accessed 04-12-2023], https://leetcode.com/ , 2010
LeetCode · 2010
Earlier work this paper cites.
“RAPL: memory power estimation and capping”
Howard David et al · 2010
Earlier work this paper cites.
“The New Linux ’ perf ’ Tools”, 2010
Arnaldo de Melo and Red Hat · 2010
Earlier work this paper cites.
“Stochastic superoptimization”
Eric Schkufza, Rahul Sharma and Alex Aiken · 2013
Earlier work this paper cites.
“A systematic literature review of green software metrics”, 2014
Patricia Lago, Qing Gu and Paolo Bozzelli · 2014
Earlier work this paper cites.
“Learning both Weights and Connections for Efficient Neural Networks”
Song Han, Jeff Pool, John Tran and William. Dally · 2015
Earlier work this paper cites.
“An Analysis of Deep Neural Network Models for Practical Applications”
Alfredo Canziani, Adam Paszke and Eugenio Culurciello · 2016
Earlier work this paper cites.
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Dongsheng Li, Xiaoming Chen, Matteo Becci and Zhi Zong · 2016
Earlier work this paper cites.
“Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding”
Dally Han Mao · 2016
Earlier work this paper cites.
“Angelix: scalable multiline program patch synthesis via symbolic analysis”
Sergey Mechtaev, Jooyong Yi and Abhik Roychoudhury · 2016
Earlier work this paper cites.
“Program Synthesis”
Sumit Gulwani, Oleksandr Polozov and Rishabh Singh · 2017
Earlier work this paper cites.
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Tianji Yang, Yu-Hsin Chen, Joel Emer and Vivienne Sze · 2017
Cited alongside, same era.
“Energy efficiency across programming languages: how do energy, time, and memory relate?”
Rui Pereira et al · 2017
Cited alongside, same era.
“Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer”
Noam Shazeer et al · 2017
Cited alongside, same era.
URL: https://openai.com/research/ai-and-compute
Amodei and Hernandez, 2018 · 2018
Cited alongside, same era.
“Data Center Cooling using Model-predictive Control”
Nevena Lazic et al · 2018
Cited alongside, same era.
“Constructing Fast Network through Deconstruction of Convolution”
“LooPy: interactive program synthesis with control structures”
Kasra Ferdowsifard et al · 2021
Later among the works it cites.
“Sustainable ai: Environmental implications, challenges and opportunities”
Carole-Jean Wu et al · 2022
Later among the works it cites.
“World Energy Outlook 2022”, 2022
IEA · 2022
Later among the works it cites.
“Assessing the Quality of GitHub Copilot’s Code Generation”, 2022
Burak Yetiştiren, Eray Tüzün and Işık Özsoy · 2022
Later among the works it cites.
“Measuring the Carbon Intensity of AI in Cloud Instances”
Dodge et al · 2022
Later among the works it cites.
“Automated Code generation, status, quality and validity in 2022”, 2022
Esko Malinen and Jesse Nygrén · 2022
Later among the works it cites.
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Yunho Jeon and Junmo Kim · 2018
Cited alongside, same era.
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Zheng Qin et al · 2018
Cited alongside, same era.
“MobileNetV2: Inverted Residuals and Linear Bottlenecks”
Mark Sandler et al · 2018
Cited alongside, same era.
“ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design”
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng and Jian Sun · 2018
Cited alongside, same era.
“Energy and Policy Considerations for Deep Learning in NLP”
Emma Strubell, Ananya Ganesh and Andrew McCallum · 2019
Cited alongside, same era.
“Write, execute, assess: Program synthesis with a repl”
Kevin Ellis et al · 2019
Cited alongside, same era.
“The Information Factories: Data centers are chewing up vast amounts of energy — So researchers are trying to make them more efficient”
Nicola Jones · 2019
Cited alongside, same era.
“Green AI: Do Deep Learning Frameworks Have Different Costs?”, 2022
Stefanos Georgiou et al · 2022
Later among the works it cites.
“An Empirical Evaluation of GitHub Copilot’s Code Suggestions”
Nhan Nguyen and Sarah Nadi · 2022
Later among the works it cites.
“Jigsaw: Large Language Models meet Program Synthesis”
Naman Jain et al · 2022
Later among the works it cites.
“A systematic evaluation of large language models of code”
Frank. Xu, Uri Alon, Graham Neubig and Vincent Hellendoorn · 2022
Later among the works it cites.
“Green AI: Do Deep Learning Frameworks Have Different Costs?”
Stefanos Georgiou et al · 2022
Later among the works it cites.
“Tracking Clean Energy Progress 2023”, 2023
IEA · 2023
Later among the works it cites.
“Data Centres Metered Electricity Consumption 2022” ISSN: 2811-5422, 2023
Ireland Central Statistics Office · 2023
Later among the works it cites.
“Trends in AI inference energy consumption: beyond the performance-vs-parameter laws of deep learning”
Radosvet Desislavov, Fernando Martinez-Plumed and Jose Hernander-Orallo · 2023
Later among the works it cites.
“Carbon Dependencies in Datacenter Design and Management”
Bilge Acun et al · 2023
Later among the works it cites.
“Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning”
Radosvet Desislavov, Fernando Martínez-Plumed and José Hernández-Orallo · 2023
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“The Framework Tax: Disparities Between Inference Efficiency in Research and Deployment”, 2023
Fernandez et al · 2023
Later among the works it cites.
“How is the speed of code review affected by activity, usage and code quality?”, 2023
William Brown · 2023
Later among the works it cites.
“Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT”, 2023
Burak Yetiştiren, Işık Özsoy, Miray Ayerdem and Eray Tüzün · 2023
Later among the works it cites.
“A Dataset for Analysis of Quality Code and Toxic Comments”, 2023, pp. 559–574
Jaime Sayago, Gustavo Chango, Ricardo Pérez-Castillo and Mario Piattini · 2023
Later among the works it cites.
“ESTIMATING THE CARBON FOOTPRINT OF BLOOM, A 176B PARAMETER LANGUAGE MODEL”
Luccioni, Viguier and Ligozat · 2023
Later among the works it cites.
“Investigating the Use of Natural Language Processing for Automated Code Generation”
Tapomoy Adhikari · 2023
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“Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT”, 2023
Burak Yetistiren, Isık Özsoy, Miray Ayerdem and Eray Tüzün · 2023
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