Before revision:  We apply the framework to study the execution behaviour of SqueezeNet on the Maxwell GPU of the NVidia Jetson TX1 , on an image classification task (also known as inference) and demonstrate the ability to measure energy of specific layers of the neural network .
After revision:  We apply the framework to study the execution behaviour of current deep learning models at a fine-grained level (or specific layers) on image processing tasks. In addition, we build an initial multi-variable linear regression model to predict energy consumption of unseen neural network models based on the number of SIMD instructions executed and main memory accesses of the CPU cores of the TX1 , on an image classification task (also known as inference) and demonstrate the ability to measure energy of specific layers of the neural network .