CNS Core: Small: Re-engineering Applications for Tensor Processing Units

为了克服用于机器学习(ML)和人工智能(AI)计算应用的传统处理器的低效，在所有类型的计算机系统中直接出现了提供运算符来计算张量/矩阵的硬件加速器。任何自然地消耗和产生张量/矩阵的应用程序都可以利用这些加速器。然而，由于缺乏适当的编程接口和系统支持，这些加速器可能仅用于ML/AI应用程序。该项目将弥补使这些加速器可用于更多应用的差距。这个项目将重新设计应用程序，以使用这些加速器来提高性能和能量。该项目将(1)使用商业化的ML/AI加速器(例如，Google的张量处理单元(TPU))构建真实的系统，(2)开发一个编程接口，允许程序员在所构建的系统上创建任何类型的应用程序，(3)重新设计应用程序的算法，以有效地使用TPU提供的运算符，(4)合成库函数和运行时系统，以高效地处理任务，但对程序员隐藏复杂性，以及(5)重新设计存储子系统，以更有效地提供数据。通过上述任务，本项目将展示其面临的挑战，并分析其推广应用的潜力。该项目如果成功，将为主题方案股提供第一个通用的方案编制平台。通过向公众提供结果，该项目将适用于所有依赖张量/矩阵数据集的计算/分析的研究领域。事实上，这个项目的目标应用已经涵盖了数据库系统、生物信息学算法和物理模拟。该项目还将通过课堂教学、出版物、讲座和本科生暑期实习为普通受众、跨学科研究人员和少数群体提供研究/学习机会。研究成果将通过同行评议的会议和期刊论文得出。开发的代码和硬件平台的配置将在研究成果发布后通过第三方存储库服务(https://github.com/escalab/))公开提供。云数据存储服务将用于存储所有原始实验数据、源代码副本和应用程序数据集至少三年，并根据适当的要求提供。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。

To overcome the inefficiency of conventional processors for machine learning (ML) and artificial intelligence (AI) computing applications, hardware accelerators that provide operators to compute on tensors/matrices directly emerge in all types of computer systems. Any application that naturally consumes and produces tensors/matrices may take advantage of these accelerators. However, these accelerators might only be used for ML/AI applications due to the lack of an appropriate programming interface and system support. This project will bridge the gap of making these accelerators available for more applications. This project will redesign applications to use these accelerators to improve performance and energy.This project will (1) build real systems using commercialized ML/AI accelerators (e.g., Google's Tensor Processing Units (TPUs)), (2) develop a programming interface that allows programmers to create any type of application on the built system, (3) redesign the algorithms of applications to use efficiently the operators that TPUs offer, (4) compose library functions and runtime systems to process efficiently tasks but hide complexity from programmers, and (5) revisit the design of the storage subsystem to supply data more efficiently. Through these aforementioned tasks, this project will demonstrate the challenges and analyze the potentials of extending the applications of TPUs. This project, if successful, will provide the first general-purpose programming platform for TPUs. By making the outcomes available to the public, this project will be available to all research areas that depend on computation/analytics of tensor/matrix datasets. In fact, the target applications in this project already cover database systems, bioinformatics algorithms and physics simulations. This project will also offer research/learning opportunities for a general audience, interdisciplinary researchers, and minority groups by classroom teaching, publications, talks and undergraduate summer interns.The research outcomes will be made through peer-reviewed conference and journal papers. The code developed and the configurations of hardware platforms will be publicly available through third party repository services (https://github.com/escalab/) after the research outcome is published. A cloud data storage service will be used to store all raw experimental data, copies of source code and applications datasets for at least three years and make them available upon appropriate requests.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.