We design, implement, and evaluate DeepEverest, a system for the efficient execution of interpretation by example queries over the activation values of a deep neural network. DeepEverest consists of an efficient indexing technique and a query execution algorithm with various optimizations. We prove that the proposed query execution algorithm is instance optimal. Experiments with our prototype show that DeepEverest, using less than 20% of the storage of full materialization, significantly accelerates individual queries by up to 63x and consistently outperforms other methods on multi-query workloads that simulate DNN interpretation processes.
翻译:我们设计、实现并评估DeepEverest,这是一种用于在深度神经网络的激活值上执行解释性查询的高效系统。DeepEverest由一种高效索引技术和一种带有各种优化的查询执行算法组成。我们证明了该查询执行算法是最佳实例的。与我们的原型的实验表明,DeepEverest仅使用完全材料化存储的不到20%就能将单个查询加速多达63倍,并且在模拟DNN解释过程的多查询工作负载上始终优于其他方法。