Theory and algorithms

Deep Learning has become extremely popular to solve many supervised machine learning problems. This standardization of machine learning, namely computing on GPU a stochastic gradient descent is not only a plague for science but also a disaster in terms of power consumption. Recently, a growing interest is observed in the deep learning hype in order to reduce computational cost by designing lighter architectures. Several approaches for reducing the computational effort of NNs have been proposed (e.g. binarized networks or pruning methods). Moreover, promising strategies propose to select the connectivity of the network, or more generally the architecture, during the training process.

GreenAI UPPA expects to address these issues, based on both a theoretical and practical machine/deep learning analysis of standard pipeline and new paradigms. More precisely, we propose alternatives to standard deep learning pipelines in order to rethink the learning process and show how mathematical statistics could help us to select lighter algorithms and reduce training, inference complexity and environmental impact of machine learning.

Measure the hungriness of your deep learning: We measure the power consumption of recent architectures on different hardwares through our AIPowerMeter based on RAPL and nvidia-smi.



We have a long experience in creating training content on machine learning and deep learning towards student and industrial learners.