The Power of Training: How Different Neural Network Setups Influence the Energy Demand

arXiv:2401.01851v2 Announce Type: replace
Abstract: This work offers a heuristic evaluation of the effects of variations in machine learning training regimes and learning paradigms on the energy consumption of computing, especially HPC hardware with a life-cycle aware perspective. While increasing data availability and innovation in high-performance hardware fuels the training of sophisticated models, it also fosters the fading perception of energy consumption and carbon emission. Therefore, the goal of this work is to raise awareness about the energy impact of …

abstract arxiv availability computing consumption cs.ai cs.lg cs.pf data demand effects energy evaluation hardware hpc influence innovation life machine machine learning network neural network performance perspective power training type work