Mitigating carbon footprint for knowledge distillation based deep learning model compression.

ABSTRACT: Deep learning techniques have recently demonstrated remarkable success in numerous domains. Typically, the success of these deep learning models is measured in terms of performance metrics such as accuracy and mean average precision (mAP). Generally, a model's high performance is highly valued, but it frequently comes at the expense of substantial energy costs and carbon footprint emissions during the model building step. Massive emission of CO2 has a deleterious impact on life on earth in general and is a serious ethical concern that is largely ignored in deep learning research. In this article, we mainly focus on environmental costs and the means of mitigating carbon footprints in deep learning models, with a particular focus on models created using knowledge distillation (KD). Deep lear

SUBMITTER: Rafat K 

PROVIDER: S-EPMC10184906 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature