  Deep Learning (DL) applications are gaining momentum in the realm of
Artificial Intelligence, particularly after GPUs have demonstrated remarkable
skills for accelerating their challenging computational requirements. Within
this context, Convolutional Neural Network (CNN) models constitute a
representative example of success on a wide set of complex applications,
particularly on datasets where the target can be represented through a
hierarchy of local features of increasing semantic complexity. In most of the
real scenarios, the roadmap to improve results relies on CNN settings involving
brute force computation, and researchers have lately proven Nvidia GPUs to be
one of the best hardware counterparts for acceleration. Our work complements
those findings with an energy study on critical parameters for the deployment
of CNNs on flagship image and video applications: object recognition and people
identification by gait, respectively. We evaluate energy consumption on four
different networks based on the two most popular ones (ResNet/AlexNet): ResNet
(167 layers), a 2D CNN (15 layers), a CaffeNet (25 layers) and a ResNetIm (94
layers) using batch sizes of 64, 128 and 256, and then correlate those with
speed-up and accuracy to determine optimal settings. Experimental results on a
multi-GPU server endowed with twin Maxwell and twin Pascal Titan X GPUs
demonstrate that energy correlates with performance and that Pascal may have up
to 40% gains versus Maxwell. Larger batch sizes extend performance gains and
energy savings, but we have to keep an eye on accuracy, which sometimes shows a
preference for small batches. We expect this work to provide a preliminary
guidance for a wide set of CNN and DL applications in modern HPC times, where
the GFLOPS/w ratio constitutes the primary goal.
