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 SqueezeNet on the Maxwell GPU of the NVidia Jetson TX1 with an average relative test error rate of 8.04 +/- 5.96 \%. Surprisingly, we find that it is possible to refine the model to predict the number of SIMD instructions and main memory accesses solely from the application's Multiply-Accumulate (MAC) counts, hence, eliminating the need for actual measurements. Our predicted results demonstrate 7.08 +/- 6.0 \% average relative error over actual energy measurements of all 11 networks tested, except MobileNet. By including MobileNet the average relative test error increases to 17.33 +/- 12.2 \% .