Daniel Rothchild
University of California, Berkeley
20 Papers
73 Citations
Daniel Rothchild is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Scheduling (computing). The author has an hindex of 9, co-authored 18 publications. Previous affiliations of Daniel Rothchild include Harvard University.
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Papers
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
David A. Patterson,Joseph E. Gonzalez,Urs Holzle,Quoc V. Le,Chen Liang,Lluís-Miquel Munguía,Daniel Rothchild,David R. So,Maud Texier,Jeffrey Dean +9 more
TL;DR: In this article , the authors show four best practices to reduce ML training energy and carbon dioxide emissions, and they predict that by 2030, total carbon emissions from training will decline significantly.
Strongly lensed SNe Ia in the era of LSST: observing cadence for lens discoveries and time-delay measurements
S. Huber,S. Huber,Sherry H. Suyu,Sherry H. Suyu,Sherry H. Suyu,U. M. Noebauer,U. M. Noebauer,Vivien Bonvin,Daniel Rothchild,James H. H. Chan,Humna Awan,Frederic Courbin,Markus Kromer,Markus Kromer,Philip J. Marshall,Masamune Oguri,Masamune Oguri,T. Ribeiro +17 more
TL;DR: In this article, a detailed analysis of different observing strategies for the LSST, and quantification of their impact on time-delay measurement between multiple images of LSNe Ia was presented.
•Posted Content
Carbon Emissions and Large Neural Network Training.
David A. Patterson,Joseph E. Gonzalez,Quoc V. Le,Chen Liang,Lluís-Miquel Munguía,Daniel Rothchild,David R. So,Maud Texier,Jeffrey Dean +8 more
TL;DR: In this article, the authors calculate the energy use and carbon footprint of several recent large models, including T5, Meena, GShard, Switch Transformer, and GPT-3, and refine earlier estimates for the neural architecture search that found evolved transformer.
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Strongly lensed SNe Ia in the era of LSST: observing cadence for lens discoveries and time-delay measurements
S. Huber,Sherry H. Suyu,Sherry H. Suyu,Sherry H. Suyu,U. M. Noebauer,U. M. Noebauer,Vivien Bonvin,Daniel Rothchild,James H. H. Chan,Humna Awan,Frederic Courbin,Markus Kromer,Markus Kromer,Philip J. Marshall,Masamune Oguri,Masamune Oguri,T. Ribeiro +16 more
TL;DR: In this paper, the authors present a detailed analysis of different observing strategies for the LSST, and quantify their impact on time-delay measurement between multiple images of LSNe Ia.
•Posted Content
SqueezeWave: Extremely Lightweight Vocoders for On-device Speech Synthesis
TL;DR: SqueezeWave is presented, a family of lightweight vocoders based onWaveGlow that can generate audio of similar quality to WaveGlow with 61x - 214x fewer MACs.
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