Poster
in
Workshop: Tackling Climate Change with Machine Learning: Global Perspectives and Local Challenges
Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models
Johannes Getzner · Bertrand Charpentier · Stephan Günnemann
Keywords: [ Data mining ] [ Classification, regression, and supervised learning ] [ Power and energy systems ]
Modern machine learning models have started to consume incredible amounts of energy, thus incurring large carbon footprints (Strubell et al., 2019). To address this issue, we have created an energy estimation pipeline, which allows practitioners to estimate the energy needs of their models in advance, without actually running or training them. We accomplished this, by collecting high-quality energy data and building a first baseline model, capable of predicting the energy consumption of DL models by accumulating their estimated layer-wise energies.