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Energy Predictive Models for Convolutional Neural Networks on Mobile Platforms

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Journal Article

Rodrigues, Crefeda Faviola, G. Riley, and M. Luján. 2020. “Energy Predictive Models for Convolutional Neural Networks on Mobile Platforms.” ArXiv. https://doi.org/10.13140/RG.2.2.15224.80644.

This work provides a comprehensive analysis of building regression-based predictive models for deep learning on mobile devices, based on empirical measurements gathered from the SyNERGY framework, and shows that simple layer-type features achieve a model complexity of 4 to 32 times less for convolutional layer predictions for a similar accuracy compared to predictive models using more complex features adopted by previous approaches. Energy use is a key concern when deploying deep learning models on mobile and embedded platforms. Current studies develop energy predictive models based on application-level features to provide researchers a way to estimate the energy consumption of their deep learning models. This information is useful for building resource-aware models that can make efficient use of the hard-ware resources. However, previous works on predictive modelling provide little insight into the trade-offs involved in the choice of features on the final predictive model accuracy and model complexity. To address this issue, we provide a comprehensive analysis of building regression-based predictive models for deep learning on mobile devices, based on empirical measurements gathered from the SyNERGY framework.Our predictive modelling strategy is based on two types of predictive models used in the literature:individual layers and layer-type. Our analysis of predictive models show that simple layer-type features achieve a model complexity of 4 to 32 times less for convolutional layer predictions for a similar accuracy compared to predictive models using more complex features adopted by previous approaches. To obtain an overall energy estimate of the inference phase, we build layer-type predictive models for the fully-connected and pooling layers using 12 representative Convolutional NeuralNetworks (ConvNets) on the Jetson TX1 and the Snapdragon 820using software backends such as OpenBLAS, Eigen and CuDNN. We obtain an accuracy between 76% to 85% and a model complexity of 1 for the overall energy prediction of the test ConvNets across different hardware-software combinations.

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