Sustainable AI

Chapter 1. Quantifying Efficiency of Deep Learning

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Recent advancements in ML/AI methods have enabled remarkable progress in multiple application domains such as computer vision, natural language processing, drug discovery and entertainment. In particular, these advancements are due to the accelerated progress in Deep Learning (DL) which, in turn, has coincided with the access to big data and large scale compute. In this chapter, we will formalise redundancies in DL pipelines at algorithmic and behavioural levels using the concept of AI-waste, distinguish compute-energy-carbon efficiency of DL and present tools to quantify the resource efficiency of DL pipelines.

AI-waste

Machine Learning (ML) in its simplest formulation is the process of learning from data. Modern DL methods take this to another level, in terms of the volume of data and the size of models used to learn from data.¹ The data-driven approach necessitates training of ...