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、实现并评估了DeepEverest系统，该系统能够高效地执行针对深度神经网络激活值的“通过示例进行解释”型查询。DeepEverest包含高效的索引技术以及采用多种优化策略的查询执行算法。我们证明了所提出的查询执行算法是实例最优的。原型实验表明，与完整物化存储相比，DeepEverest在仅使用不到20%存储空间的情况下，可将单次查询速度提升高达63倍，并且在模拟深度神经网络解释过程的多查询工作负载中持续优于其他方法。