  Since the emergence of Deep Neural Networks (DNNs) as a prominent technique
in the field of computer vision, the ImageNet classification challenge has
played a major role in advancing the state-of-the-art. While accuracy figures
have steadily increased, the resource utilisation of winning models has not
been properly taken into account. In this work, we present a comprehensive
analysis of important metrics in practical applications: accuracy, memory
footprint, parameters, operations count, inference time and power consumption.
Key findings are: (1) power consumption is independent of batch size and
architecture; (2) accuracy and inference time are in a hyperbolic relationship;
(3) energy constraint is an upper bound on the maximum achievable accuracy and
model complexity; (4) the number of operations is a reliable estimate of the
inference time. We believe our analysis provides a compelling set of
information that helps design and engineer efficient DNNs.
