  In resource limited computing systems, sequence prediction models must
operate under tight constraints. Various models are available that cater to
prediction under these conditions that in some way focus on reducing the cost
of implementation. These resource constrained sequence prediction models, in
practice, exhibit a fundamental tradeoff between the cost of implementation and
the quality of its predictions. This fundamental tradeoff seems to be largely
unexplored for models for different tasks. Here we formulate the necessary
theory and an associated empirical procedure to explore this tradeoff space for
a particular family of machine learning models such as deep neural networks. We
anticipate that the knowledge of the behavior of this tradeoff may be
beneficial in understanding the theoretical and practical limits of creation
and deployment of models for resource constrained tasks.
