EAGER: Collaborative Research: Inexactness and Data-Awareness in Network Stacks for Distributed Machine Learning

EAGER：协作研究：分布式机器学习网络堆栈中的不精确性和数据感知

• 批准号: 1940109
• 负责人: Aditya Akella
• 金额: $ 15万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940109&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Inexactness; Data

The architectures underlying modern network hardware and software have their roots in designs that were developed decades ago. Even though these architectures have evolved in many ways over the years, they remain unchanged in two key aspects: (1) They support ?exact? or complete/absolute reliable communication (either hop-by-hop, or end-to-end, or both); (2) They adhere to strict layering, and the resulting encapsulation and interfaces hide from lower network layers the semantics of the data applications transmit over the network.These design principles place serious impediments for emerging distributed machine learning (ML) training and inference applications. These applications are seeing adoption in a wide variety of important domains, such as, computer vision, robotics, data science, graphics, and speech recognition. Two distinguishing attributes of these applications are: (1) their computations are intrinsically inexact in nature, because these applications rely on computing or utilizing statistical models, and (2) their input and intermediate data have well-defined structure, i.e., tensors, or multi-dimensional arrays of typed data. Give these attributes, enforcing exact communication in a data semantics-unaware fashion limits the potentially enormous benefits of embracing inexactness in these approximate applications.This project explores co-designing ML applications with layers of the network software and hardware stack to allow application-driven cross-layer optimization for energy efficiency, hardware density/capacity, and performance. Given an application-provided overall inexactness budget, this research will explore both how to systematically apportion the budget across network layers, and how different layers can reconfigure their functionality to achieve different levels of approximation. This project will develop strawman approaches to encoding structured data and to achieving budget-driven inexact computation over it. The research will use experiments, simulations, and analysis to identify performance benefits to ML applications, and fundamental trade-offs that determine the feasibility of this approach. The resulting inexactness-aware ML software stack could drive hitherto unseen performance and accuracy improvements, and potentially drive future innovations in ML algorithms, systems, and 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.

现代网络硬件和软件的基础架构源于几十年前开发的设计。尽管这些架构多年来在许多方面发生了演变，但它们在两个关键方面保持不变：(1) 它们支持“精确”。或完整/绝对可靠的通信（逐跳或端到端或两者）； (2) 它们坚持严格的分层，并且由此产生的封装和接口向较低网络层隐藏了通过网络传输的数据应用程序的语义。这些设计原则为新兴的分布式机器学习（ML）训练和推理应用程序设置了严重障碍。这些应用程序正在广泛的重要领域得到采用，例如计算机视觉、机器人、数据科学、图形和语音识别。这些应用程序的两个显着属性是：（1）它们的计算本质上是不精确的，因为这些应用程序依赖于计算或利用统计模型，（2）它们的输入和中间数据具有明确定义的结构，即张量或类型化数据的多维数组。赋予这些属性，以数据语义无意识的方式强制执行精确通信，限制了在这些近似应用程序中接受不精确性的潜在巨大好处。该项目探索与网络软件和硬件堆栈层共同设计 ML 应用程序，以允许应用程序驱动的交叉-能源效率、硬件密度/容量和性能的层优化。考虑到应用程序提供的总体不精确预算，本研究将探讨如何跨网络层系统地分配预算，以及不同层如何重新配置​​其功能以实现不同级别的近似。该项目将开发稻草人方法来编码结构化数据并实现预算驱动的不精确计算。该研究将使用实验、模拟和分析来确定机器学习应用程序的性能优势，以及决定这种方法可行性的基本权衡。由此产生的不精确感知机器学习软件堆栈可以推动前所未有的性能和准确性改进，并有可能推动机器学习算法、系统和应用程序的未来创新。该奖项反映了 NSF 的法定使命，并通过使用基金会的知识产权进行评估，被认为值得支持。优点和更广泛的影响审查标准。