Analyzing Energy Efficiency in Neural Networks
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2020
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Haverford College. Department of Computer Science
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Thesis
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eng
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Open Access
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Abstract
Recent advances in deep learning have led to the development of state-of-the-art models with remarkable accuracy; however, previous work has shown that these results incur a high environmental cost due to their significant energy usage. Nevertheless, accuracy remains the predominant evaluation criterion for neural network performance, so much so that computationally-expensive techniques such as neural architecture searches are oftentimes employed to only moderate success. What is more, the relationship between energy usage and accuracy has been shown to be non-linear. Thus, an increase in energy usage may not necessarily lead to an increase in accuracy. This thesis surveys the current literature pertaining to energy efficiency in deep learning and proposes that future work should examine how energy usage is a distinct trade-off in neural network models.