%0 Generic %1 canziani2016analysis %A Canziani, Alfredo %A Paszke, Adam %A Culurciello, Eugenio %D 2016 %K computer deep learning models vision %T An Analysis of Deep Neural Network Models for Practical Applications %U http://arxiv.org/abs/1605.07678 %X Since the emergence of Deep Neural Networks (DNNs) as a prominent technique in the field of computer vision, the ImageNet classification challenge has played a major role in advancing the state-of-the-art. While accuracy figures have steadily increased, the resource utilisation of winning models has not been properly taken into account. In this work, we present a comprehensive analysis of important metrics in practical applications: accuracy, memory footprint, parameters, operations count, inference time and power consumption. Key findings are: (1) power consumption is independent of batch size and architecture; (2) accuracy and inference time are in a hyperbolic relationship; (3) energy constraint is an upper bound on the maximum achievable accuracy and model complexity; (4) the number of operations is a reliable estimate of the inference time. We believe our analysis provides a compelling set of information that helps design and engineer efficient DNNs.

@misc{canziani2016analysis, abstract = {Since the emergence of Deep Neural Networks (DNNs) as a prominent technique in the field of computer vision, the ImageNet classification challenge has played a major role in advancing the state-of-the-art. While accuracy figures have steadily increased, the resource utilisation of winning models has not been properly taken into account. In this work, we present a comprehensive analysis of important metrics in practical applications: accuracy, memory footprint, parameters, operations count, inference time and power consumption. Key findings are: (1) power consumption is independent of batch size and architecture; (2) accuracy and inference time are in a hyperbolic relationship; (3) energy constraint is an upper bound on the maximum achievable accuracy and model complexity; (4) the number of operations is a reliable estimate of the inference time. We believe our analysis provides a compelling set of information that helps design and engineer efficient DNNs.}, added-at = {2021-12-05T15:38:17.000+0100}, author = {Canziani, Alfredo and Paszke, Adam and Culurciello, Eugenio}, biburl = {https://www.bibsonomy.org/bibtex/2d0e02c3192ecec48e4bf29424a7dcc00/sdo}, description = {An Analysis of Deep Neural Network Models for Practical Applications}, interhash = {b0e2dc2bcbc54c16fc480bab9eb7568e}, intrahash = {d0e02c3192ecec48e4bf29424a7dcc00}, keywords = {computer deep learning models vision}, note = {Avaliable at \url{http://arxiv.org/abs/1605.07678}}, timestamp = {2021-12-05T15:38:17.000+0100}, title = {An Analysis of Deep Neural Network Models for Practical Applications}, url = {http://arxiv.org/abs/1605.07678}, year = 2016 }