CAREER: Rethink and Redesign of Analytics Databases for Machine Learning Model Serving

迫切需要将人工智能应用于交互式应用，如供应链预测、信用卡欺诈检测、客户服务聊天机器人、应急响应和医疗咨询。数据库管理这些应用程序的很大一部分数据。然而，由于现有数据库缺乏对深度神经网络推理的支持，人工智能通常是由一个单独的机器学习过程提供的。因此，解耦系统之间的数据传输显著增加了延迟，这使得满足交互应用的时间限制变得具有挑战性。这种分离还会使应用程序开发和系统管理复杂化。这项研究将使来自数据库的本地深度神经网络模型推理能够消除跨系统的开销。它将提供一个统一的表示，以弥合数据查询和深度神经网络模型之间的差距。最终，该项目将提供一个新的数据库系统，以促进交互式智能应用程序。这项研究将为亚利桑那州K-12代表不足的学生和难民青年举办大数据魔术周活动提供支持。它将被用作一个平台，为亚利桑那州立大学挑选的本科生参加国际研究比赛做准备。它还将与亚利桑那州立大学关于机器学习的数据密集型系统的研究生课程相结合。研究目标是重新思考和重新设计分析数据库，以统一数据查询和机器学习模型推理，特别是深层神经网络模型推理。研究人员将目标划分为三个协同研究推动力。首先，研究将开发通过统一的两级中间表示来连接机器学习推理和关系代数处理的方法。它将支持将所有规模的机器学习模型逐步降低为关系代数表达式和灵活但可分析的函数。此外，它还促进了数据查询和模型推理的多目标联合优化，以实现最佳的延迟、准确性和资源利用率权衡。其次，该项目将进一步提供提前代码生成，以减少模型推理的延迟。生成的代码将允许运行时物理优化，例如中间数据的物化和批处理大小调整，这些都是适应动态查询频率的。第三，这项研究将通过基于张量块的大小、相似性和其他特定于模型的属性对张量块进行索引和聚类来提供精确度感知的存储优化。这项研究将在查询处理和模型推理之间建立新的联系。如果成功，该项目将大大减少一大类时间关键型数据密集型人工智能应用程序的端到端延迟。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

It is urgent to apply artificial intelligence to interactive applications, such as supply-chain prediction, credit card fraud detection, customer service chatbot, emergency response, and healthcare consulting. Databases manage a significant portion of data for these applications. However, due to the lack of support for deep neural network inference in existing databases, artificial intelligence is usually provided by a separate process for machine learning. As a result, data transfer between decoupled systems significantly increases the latency, making it challenging to meet the time constraints of interactive applications. Such decoupling also complicates application development and system management. This research will enable native deep neural network model inferences from databases to eliminate cross-system overheads. It will provide a unified representation to bridge the gap between data queries and deep neural network models. Ultimately, the project will deliver a novel database system to facilitate interactive intelligent applications. The research will support a Big Data Magic Week activity for K-12 underrepresented students and refugee youths in Arizona. It will be used as a platform to prepare selected undergraduate students in Arizona State University for international research competitions. It will also be integrated with a graduate-level course on data-intensive systems for machine learning at Arizona State University.The research objective is to rethink and redesign analytics databases to unify data queries and machine learning model inferences, particularly deep neural network model inferences. The investigator divides the aim into three synergistic research thrusts. First, the research will develop methods to bridge the machine learning inference and the relational algebra processing through a unified two-level intermediate representation. It will support the progressive lowering of all-scale machine learning models into relational algebra expressions and flexible yet analyzable functions. Moreover, it facilitates multi-objective co-optimization of data queries and model inferences for optimal latency, accuracy, and resource utilization trade-offs. Second, the project will further provide ahead-of-time code generation to reduce the latency of model inferences. The generated code will allow runtime physical optimizations such as materialization of intermediate data and batch size tuning, which are adaptive to dynamic query frequencies. Third, the research will provide accuracy-aware storage optimizations by indexing and clustering tensor blocks based on their magnitudes, similarity, and other model-specific properties. This research will establish new connections between query processing and model inferences. If successful, the project will dramatically reduce end-to-end latency for a broad class of time-critical data-intensive artificial intelligence applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.