Print Email Facebook Twitter Green AI Title Green AI: An empirical study Author Yarally, Tim (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor van Deursen, A. (mentor) Cruz, Luis (mentor) Weinmann, M. (mentor) Feitosa, Daniel (mentor) Degree granting institution Delft University of Technology Programme Computer Science Date 2022-07-01 Abstract In this work, we look at the intersection of Sustainable Software Engineering and AI engineering known as Green AI. AI computing is rapidly becoming more expensive, calling for a change in design philosophy. We consider both training and inference of neural networks used for image vision; to reveal energy-efficient practices in an exploratory fashion.First of all, we examine a modern algorithm for hyperparameter optimisation and compare this to two baseline methods. We find that the baseline algorithms perform considerably worse despite their wide usage and argue that they should not be used when training large models. Furthermore, we look at the layer structure of convolutional networks and conclude that the convolutional layers have the largest influence on the total consumption. We report increases of up to 95% with only marginal improvements in accuracy. Therefore we recommend developers to reduce their network architectures as long as the performance stays within a reasonable margin. Second, we present a study focused on the inference phase of the deep learning pipeline. We look at the effect of batching for image classification requests. To facilitate the data collection, we make use of a simulated queue and the Pytorch framework. We find that batching has a significant impact on the energy consumption, but the magnitude of this impact can vary a lot for different models. Our recommendation is to treat the batch size as an inference parameter that needs to be tuned first. Additionally, we highlight how the energy consumption of image vision networks has evolved over the past decade. Presenting the findings together with the performance of these networks shows a steady, upward energy trend accompanied by a decreasing slope for the accuracy. The only exception is the model ShuffleNetV2. We mention the design principles that went into the development of this network and present it as a start for future research. Subject Green AISoftware EngineeringHyperparameter OptimizationImage Classification To reference this document use: http://resolver.tudelft.nl/uuid:a3de8838-8e9a-4c70-8a04-a9f296be7c84 Part of collection Student theses Document type master thesis Rights © 2022 Tim Yarally Files PDF GreenAI_TER_Yarally.pdf 6.84 MB Close viewer /islandora/object/uuid:a3de8838-8e9a-4c70-8a04-a9f296be7c84/datastream/OBJ/view