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

机器学习（ML）在某些情况下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的成功得益于最近ML系统的发展，该系统为人们提供了高级编程接口，可以在现代硬件平台上对各种ML模型进行原型设计。然而，在不同的实际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现所需的性能优化。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)以端到端方式应用发现的系统优化的新方法；(3)结合系统和机器学习优化，实现快速准确的机器学习计算。随着机器学习技术越来越接近最终用户，越来越融入当今社会，所提出的工作可以有效地降低现代机器学习技术的能耗和财务成本。提出的研究的关键改进将沿着两个方向出现：(1)用现代硬件平台上机器学习计算的系统优化的自动生成、验证和应用取代当今机器学习系统中使用的手动设计的性能优化；(2)通过降低开发和部署机器学习应用程序的货币成本，使机器学习技术民主化。该项目还包括外展活动，以吸引目前在计算机领域代表性不足的学生。这些活动的关键是拥抱机器学习系统研究的跨学科性质，它跨越了计算机系统、编译器、编程语言和机器学习。这个项目的软件构件将被发布并定期维护。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.