Publication Details
Energy Complexity Model for Convolutional Neural Networks
energy complexity, neural networks
The energy efficiency of hardware implementations of convolutional neural
networks (CNNs) is critical to their widespread deployment in low-power mobile
devices. Recently, a plethora of methods have been proposed providing
energy-optimal mappings of CNNs onto diverse hardware accelerators. Their
estimated power consumption is related to specific implementation details and
hardware parameters, which does not allow for machine-independent exploration of
CNN energy measures. In this paper, we introduce a simplified theoretical energy
complexity model for CNNs, based on only two-level memory hierarchy that captures
asymptotically all important sources of power consumption of different CNN
hardware implementations. We calculate energy complexity in this model for two
common dataflows which, according to statistical tests, fits asymptotically very
well the power consumption estimated by the Time/Accelergy program for
convolutional layers on the Simba and Eyeriss hardware platforms. The model opens
the possibility of proving principal limits on the energy efficiency of CNN
hardware accelerators.
@inproceedings{BUT185188,
author="ŠÍMA, J. and VIDNEROVÁ, P. and MRÁZEK, V.",
title="Energy Complexity Model for Convolutional Neural Networks",
booktitle="Artificial Neural Networks and Machine Learning - ICANN 2023: 32nd International Conference on Artificial Neural Networks",
year="2023",
series="Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages="186--198",
publisher="Springer Nature Switzerland AG",
address="Heraklion",
doi="10.1007/978-3-031-44204-9\{_}16",
isbn="978-3-031-44203-2"
}