CNS Core: Medium: Accurate Anytime Learning for Energy andTimeliness in Software Systems

CNS 核心：中：随时准确学习软件系统的能量和及时性

• 批准号: 1956180
• 负责人: Shan Lu
• 金额: $ 120万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1956180&HistoricalAwards=false
• 关键词: CNS; Core; Medium; Accurate; Anytime

Modern software systems increasingly rely on deep neural networks to perform a wide range of tasks, such as natural language translation and autonomous driving. The key to their success is that deep neural networks can well approximate these difficult tasks. Unfortunately, the more accurate the approximation, the more resources required. When deployed on mobile devices or autonomous vehicles those resource needs directly impact people as the time or energy/battery required to produce an answer. This project tackles this crucial problem by developing sound engineering methods to make disciplined tradeoffs between neural network accuracy and resource usage in software systems. The proposed work will take an interdisciplinary approach. Its first thrust will design novel neural networks that efficiently produce a series of outputs, instead of just a single output, such that the output accuracy will increase with increasing resources. The second thrust will develop new resource management software that automatically adjusts both underlying system settings and one or multiple neural networks to meet high-level software accuracy and energy requirements. The third will create tools for automatically analyzing the software context where neural networks are used, inferring accuracy and resource requirements for neural networks and identifying inefficient use of neural networks.The project has the potential to improve the efficiency and reliability of software that incorporates neural networks, and hence improve people's daily life experiences. Software developers will benefit from greater flexibility in neural network design, greater assurance that the neural networks they deploy will meet their accuracy and resource requirements, and greater understanding of how the neural network impacts the rest of their software systems. Additionally, the project will create many educational opportunities through enhanced classroom projects and creation of research opportunities for undergraduates, broadening the participation in computing.All of the data, code, results, and artifacts of this project will be made publicly available through the webpage https://alert.cs.uchicago.edu/. They will be available on-line for a period of at least five years following the completion of this project.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.

现代软件系统越来越依赖深度神经网络来执行各种任务，例如自然语言翻译和自动驾驶。他们成功的关键在于深度神经网络可以很好地近似这些困难的任务。不幸的是，近似值越精确，所需的资源就越多。 当部署在移动的设备或自动驾驶车辆上时，这些资源需求直接影响人们产生答案所需的时间或能量/电池。 该项目通过开发合理的工程方法来解决这个关键问题，以便在软件系统中的神经网络准确性和资源使用之间进行纪律性的权衡。拟议的工作将采取跨学科办法。它的第一个目标是设计新型神经网络，有效地产生一系列输出，而不仅仅是单个输出，这样输出精度将随着资源的增加而提高。第二个重点是开发新的资源管理软件，自动调整底层系统设置和一个或多个神经网络，以满足高水平的软件精度和能源需求。第三个项目将创建自动分析使用神经网络的软件环境的工具，推断神经网络的准确性和资源需求，并识别神经网络的低效使用。该项目有可能提高包含神经网络的软件的效率和可靠性，从而改善人们的日常生活体验。 软件开发人员将受益于神经网络设计的更大灵活性，更好地保证他们部署的神经网络将满足其准确性和资源要求，以及更好地了解神经网络如何影响其软件系统的其余部分。 此外，该项目将通过增强课堂项目和为本科生创造研究机会来创造许多教育机会，扩大对计算的参与。该项目的所有数据，代码，结果和工件将通过https://alert.cs.uchicago.edu/网页公开。该奖项反映了NSF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。

项目成果

Proxima: accelerating the integration of machine learning in atomistic simulations

• DOI: 10.1145/3447818.3460370
• 发表时间: 2021-06
• 期刊: Proceedings of the 35th ACM International Conference on Supercomputing
• 作者: Yuliana Zamora;Logan T. Ward;G. Sivaraman;Ian T. Foster;H. Hoffmann

Are Machine Learning Cloud APIs Used Correctly?

• DOI: 10.1109/icse43902.2021.00024
• 发表时间: 2021-05
• 期刊: 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)
• 作者: Chengcheng Wan;Shicheng Liu;H. Hoffmann;M. Maire;Shan Lu

GOAL: Supporting General and Dynamic Adaptation in Computing Systems

• DOI: 10.1145/3563835.3567655
• 发表时间: 2022-11
• 期刊: Proceedings of the 2022 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software
• 作者: Ahsan Pervaiz;Yao-Hsiang Yang;Adam Duracz;F. Bartha;R. Sai;Connor Imes;Robert Cartwright;K. Palem;Shan Lu;Henry Hoffmann

Growing Efficient Deep Networks by Structured Continuous Sparsification

• 发表时间: 2020-07
• 期刊: ArXiv
• 作者: Xin Yuan;Pedro H. P. Savarese;M. Maire

Automated Testing of Software that Uses Machine Learning APIs

• DOI: 10.1145/3510003.3510068
• 发表时间: 2022-05
• 期刊: 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)
• 作者: Chengcheng Wan;Shicheng Liu;Sophie Xie;Yifan Liu;H. Hoffmann;M. Maire;Shan Lu