Abstract
Recently convolutional neural networks (CNNs) have drawn much attentions and been widely applied on image recognition; therefore, the complexity of computation and energy consumption have become a big issue for deploying CNNs, especially on embedded systems or other battery-powered mobile devices. Apart from reducing the complexity of network computations, if we could estimate the energy consumptions of the given network configuration before train or test phases, we would realize that whether the CNNs can be deployed on mobile devices or not. As the result, we propose a predictive energy model to effectively predict the energy consumption of a CNN. In this work, first we analyze the relation between different network configurations and kernel functions operations reported by NVIDIA profiler tool in detail, and then based on the analysis, we propose a predictive energy model that could calculate an estimated energy consumption as we have the architecture of a convolutional neural network before test phases. The experiments are processed with CIFAR-10 dataset and are implemented in Caffe, and the overall error rate of our methodology for predicting energy consumption is 14.41%.