Before revision: Energy-use is a key concern when migrating current deep learning applications onto low power heterogeneous devices such as a mobile device. This is because deep neural networks are typically designed and trained on high-end GPUs or servers and require additional processing steps to deploy them on low powerdevices.
After revision: There is a huge demand for on-device execution of deep learning algorithms on mobile and embedded platforms. These devices present constraints on the application due to limited resources and power. Hence, developing energy-efficient solutions to address this issue will require innovation in algorithmic design, software and hardware. Such innovation requires benchmarking and characterization of Deep Neural Networks based on performance and energy-consumption alongside accuracy. However, current benchmarks studies in existing deep learning frameworks (for example, Caffe, Tensorflow, Torch and others) are based on performance of these applications on high-end GPUs or servers and require additional processing steps to deploy them on low powerdevices.