Equity through Access: A Case for Small-scale Deep Learning

arXiv:2403.12562v1 Announce Type: new
Abstract: The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry barrier to researchers and practitioners with limited access to resources at such scale, particularly in the Global South. In this work, we take a comprehensive …

abstract advances arxiv carbon case compute costs cs.ai cs.lg data deep learning emissions energy equity larger models resources scale small stat.ml terms through train type