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.

在资源受限的计算系统中，序列预测模型必须在严格约束下运行。我们提出了一种理论和相关的实证方法，探索一种特定类型机器学习模型（如深度神经网络）的成本与预测质量之间的权衡空间，以增加对资源受限任务模型的理论和实践限制的认识。