129 citations · 179 across the 4 of their papers we have counts for
4 papers
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
David Patterson, Joseph Gonzalez, Urs Hölzle +7
Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x…
C5T5: Controllable Generation of Organic Molecules with Transformers
Daniel Rothchild, Alex Tamkin, Julie Yu +2
Methods for designing organic materials with desired properties have high potential impact across fields such as medicine, renewable energy, petrochemical engineering, and agricult…
Carbon Emissions and Large Neural Network Training
David Patterson, Joseph Gonzalez, Quoc Le +6
The computation demand for machine learning (ML) has grown rapidly recently, which comes with a number of costs. Estimating the energy cost helps measure its environmental impact a…
SqueezeWave: Extremely Lightweight Vocoders for On-device Speech Synthesis
Bohan Zhai, Tianren Gao, Flora Xue +4
Automatic speech synthesis is a challenging task that is becoming increasingly important as edge devices begin to interact with users through speech. Typical text-to-speech pipelin…