Before revision:  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. Such steps include the use of compression techniques to scale down the network size or the provision of efficient device-specific software implementations. Migration is further aggravated by the lack of tools and the inability to measure power and performance accurately and consistently across devices. We present a novel evaluation framework for measuring energy and performance for deep neural networks using ARMs Streamline Performance Analyser integrated with standard deep learning frameworks such as Caffe and CuDNNv5 .
After revision:  This is because deep neural networks are typically designed and trained on high-end CPUs and GPUs. In this work, we introduce a benchmarking framework called "SyNERGY" to measure the energy and time of 11 representative Deep Convolutional Neural Networks on embedded platforms such as NVidia Jetson TX1. We integrate ARM's Streamline Performance Analyser integrated with standard deep learning frameworks such as Caffe and CuDNNv5 .