  There exists a plethora of techniques for inducing structured sparsity in
parametric models during the optimization process, with the final goal of
resource-efficient inference. However, few methods target a specific number of
floating-point operations (FLOPs) as part of the optimization objective,
despite many reporting FLOPs as part of the results. Furthermore, a
one-size-fits-all approach ignores realistic system constraints, which differ
significantly between, say, a GPU and a mobile phone -- FLOPs on the former
incur less latency than on the latter; thus, it is important for practitioners
to be able to specify a target number of FLOPs during model compression. In
this work, we extend a state-of-the-art technique to directly incorporate FLOPs
as part of the optimization objective and show that, given a desired FLOPs
requirement, different neural networks can be successfully trained for image
classification.
