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.

翻译：在资源受限的计算系统中，序列预测模型必须严格受限条件下运行。现有多种模型可满足此类预测需求，它们通过不同方式着重降低实现成本。实际应用中，这些资源受限的序列预测模型在实现成本与预测质量之间存在根本性权衡。这一根本性权衡在针对不同任务的模型中似乎尚未得到充分探索。本文针对深度神经网络等特定机器学习模型族，构建了探索该权衡空间所需的理论框架及配套实证流程。我们预期，对该权衡行为特性的认知，将有助于理解资源受限任务中模型创建与部署的理论及实践极限。