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架构源于几十年前的设计。即使这些几年来这些架构在许多方面发展了，但它们在两个关键方面保持不变：（1）它们支持吗？或完整/绝对可靠的通信（无论是跳跃还是端到端，或两者兼而有之）； （2）它们遵守严格的分层，由此产生的封装和接口隐藏在下层网络层中数据应用程序通过网络传输的语义。这些设计原理为新兴分布式机器学习（ML）培训和推理应用带来了严重的障碍。这些应用程序正在在各种重要领域中采用，例如计算机视觉，机器人技术，数据科学，图形和语音识别。这些应用程序的两个区别属性是：（1）它们的计算本质上是不确定的，因为这些应用程序依赖于计算或利用统计模型，并且（2）它们的输入和中间数据具有明确定义的结构，即张紧器或多维数据的多维阵列。给出这些属性，在数据语义上实施精确的沟通 - unaware的时尚限制了在这些近似应用中拥抱不精确的巨大好处。本项目探索了具有网络软件和硬件堆栈的共同设计的ML应用程序，以允许应用程序驱动的跨层次驾驶跨层的优化，以优化能源效率，硬件量，硬件密度，硬度/硬度/容量，和性能。鉴于应用程序提供的总体不符合性预算，这项研究将探索如何系统地分配跨网络层的预算，以及不同的层如何重新配置​​其功能以实现不同级别的近似级别。该项目将开发出稻草人的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，模拟和分析来确定对ML应用程序的绩效益处，以及决定这种方法可行性的基本权衡。由此产生的不注重的ML软件堆栈可能会推动迄今未见的性能和准确性提高，并有可能推动ML算法，系统和应用程序中未来的创新。该奖项反映了NSF的法定任务，并被认为是通过该基金会的知识功能和广泛影响的评估来评估CRETIRIA的评估，并被视为值得通过评估的支持。