CAREER: An Automated End-to-end Machine Learning System

职业：自动化的端到端机器学习系统

• 批准号: 2239351
• 负责人: Zhihao Jia
• 金额: $ 63.84万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239351&HistoricalAwards=false
• 关键词: CAREER; Automated; End; end; Machine

Machine learning (ML) has surpassed human performance in some contexts, including image classification, natural language processing, game playing, and content generation. ML’s success is enabled by the recent development of ML systems that offer high-level programming interfaces for people to prototype various ML models on modern hardware platforms. However, deploying these models in diverse, real-world computing environments requires significant engineering effort to design and implement the required performance optimizations. To address this challenge, this project explores an automated, end-to-end approach to building efficient, scalable, and sustainable ML systems for diverse ML applications and hardware platforms. The project takes a bottom-up, three-pronged approach that involves (1) automatically discovering and verifying various systems optimizations for different ML models and hardware backends; (2) new methodologies for applying the discovered systems optimizations in an end-to-end fashion; and (3) combining systems and ML optimizations for fast and accurate ML computations.As ML techniques move closer to end-users and become increasingly integrated into today’s society, the proposed work can effectively reduce the energy consumption and financial cost of modern ML techniques. The key improvements of the proposed research will arise along two axes: (1) replacing manually designed performance optimizations used in today’s ML systems with automated generation, verification, and application of systems optimizations for ML computations on modern hardware platforms; and (2) democratizing ML techniques by lowering the monetary cost of developing and deploying ML applications. The project also includes outreach activities to attract students from populations currently underrepresented in computing. Key to these activities is embracing the interdisciplinary nature of ML systems research, which spans computer systems, compilers, programming languages, and machine learning. The software artifacts of this project will be released and regularly maintained.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.

机器学习在某些方面已经超过了人类的表现，包括图像分类、自然语言处理、游戏和内容生成。ML的成功得益于ML系统的最新发展，这些系统为人们提供高级编程接口，以便在现代硬件平台上制作各种ML模型的原型。但是，在不同的真实计算环境中部署这些模型需要大量的工程工作来设计和实施所需的性能优化。为了应对这一挑战，该项目探索了一种自动化的端到端方法，为各种ML应用程序和硬件平台构建高效、可扩展和可持续的ML系统。该项目采用自下而上、三管齐下的方法，包括：(1)自动发现和验证针对不同ML模型和硬件后端的各种系统优化；(2)以端到端的方式应用所发现的系统优化；(3)将系统和ML优化相结合，以快速准确地计算ML。随着ML技术越来越接近最终用户并日益融入当今社会，所提出的工作可以有效地降低现代ML技术的能耗和财务成本。拟议研究的主要改进将沿着两个轴线出现：(1)用在现代硬件平台上自动生成、验证和应用针对ML计算的系统优化来取代当今ML系统中使用的手动设计的性能优化；以及(2)通过降低开发和部署ML应用程序的货币成本来使ML技术大众化。该项目还包括一些外联活动，以吸引目前在计算机领域任职人数不足的学生。这些活动的关键是接受ML系统研究的跨学科性质，它横跨计算机系统、编译器、编程语言和机器学习。该项目的软件产品将被发布并定期维护。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。