SHF: Small: Practical Analyses and Safe Transformations for Imperative Deep Learning Programs

SHF：小型：命令式深度学习程序的实用分析和安全转换

基本信息

批准号：
2200343
负责人：
Raffi Khatchadourian
金额：
$ 60万
依托单位：
CUNY Hunter College
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200343&HistoricalAwards=false
关键词：
SHF Small Practical Analyses Safe

项目摘要

Learning often occurs by pattern recognition. Software systems learn by using algorithms to recognize patterns and draw inferences from existing data and apply the inferences to previously unseen data. Deep Learning (DL) is a kind of machine-learning algorithm inspired by neural networks of human brains. A DL model learns decision logic from a large set of examples. A classic application is image processing, where a model may learn to recognize particular images through training with many sample images. While software systems that incorporate DL models involve large amounts of data, they still have to be efficient and responsive. This project is expected to increase DL system robustness, reliability, and scalability, positively impacting computer vision, autonomous driving, medicine, and extremism identification. Tools developed as a result of this project are also expected to democratize the Artificial Intelligence workforce, as they will assist data scientists and software engineers of varying proficiencies in writing quality DL code. Such tools can potentially contribute to a diverse, globally competitive STEM workforce and increase US economic competitiveness. This project will also promote software engineering concepts in machine learning by augmenting and creating several undergraduate and graduate courses. Dissemination will occur through publicly distributing datasets, papers, open-source software, and Open Educational Resources.DL frameworks increasingly make various tradeoffs to balance the often competing requirements of reliability, usability, and generality. Popular DL frameworks have historically embraced graph-based, deferred execution-style (low-level) Application Programming Interfaces (APIs). While efficient, (legacy) systems using such interfaces are cumbersome, error-prone, and difficult to debug, maintain, and port. Contrarily, (modern) eager execution-style DL APIs facilitate higher-level, imperative, and Object-Oriented (Python) programs that are easier to debug, less error-prone, and more extensible have consequently emerged at the expense of run-time performance. Though hybrid approaches aim to bridge the two paradigms, they necessitate a non-trivial amount of technical metadata and exhibit several limitations and known issues on the use of native program constructs. This project is expected to contribute practical analyses and safe transformations for modern imperative and Object-Oriented DL programs that markedly improve their reliability and scalability. First, various software engineering artifacts will be mined for bug fixes, (manual) refactorings (semantics-preserving source-to-source program transformations), and missed opportunities in efficiently executing imperative DL code. Then, novel analyses and refactorings for automatically (i) migrating legacy, deferred execution-style DL code to more robust imperative DL code and (ii) specifying how their otherwise eagerly-executed imperative DL code should be reliably and efficiently executed as graphs at run-time will be formulated. Finally, novel analyses for detecting performance bottlenecks and semantic errors associated with graph-based execution of imperative, otherwise eagerly-executed DL code will be designed. This contribution is significant because it fills the void of techniques, methodologies, and tools for effectively developing---and evolving long-lived---trustworthy and efficient DL systems that pervasively use imperative and Object-Oriented DL programming.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.

学习通常是通过模式识别发生的。软件系统通过使用算法来识别模式并从现有数据中得出推断，并将推论应用于以前看不见的数据，从而学习。深度学习（DL）是一种灵感来自人类大脑神经网络的机器学习算法。 DL模型从大量示例中学习决策逻辑。经典应用是图像处理，模型可以通过使用许多示例图像训练来学会识别特定图像。尽管将DL模型纳入大量数据的软件系统仍然必须有效且响应迅速。预计该项目将增加DL系统的鲁棒性，可靠性和可伸缩性，从而积极影响计算机视觉，自动驾驶，医学和极端主义识别。由于该项目而开发的工具也有望使人工智能劳动力民主化，因为它们将帮助数据科学家和软件工程师编写高质量的DL代码。这样的工具可能会导致各种各样的全球竞争性STEM劳动力并提高美国的经济竞争力。该项目还将通过增强和创建几个本科和研究生课程来促进机器学习中的软件工程概念。传播将通过公开分发数据集，论文，开源软件和开放教育资源。DL框架越来越多地进行各种权衡，以平衡可靠性，可用性和通用性的经常竞争要求。流行的DL框架历史上已经采用了基于图的延期执行式（低级）应用程序编程接口（API）。虽然有效，但使用此类界面的（传统）系统却很麻烦，容易出错，并且难以调试，维护和端口。相反，（现代）急切的执行风格的DL API促进了更易于调试，易于易错且更具扩展性的更高级别，命令和面向对象的（Python）程序，以牺牲跑步时间性能为代价。尽管混合方法旨在弥合两个范式，但它们需要非平凡的技术元数据，并在使用本机计划结构上表现出了一些局限性和已知问题。预计该项目将为现代命令和面向对象的DL计划做出实践分析和安全转换，从而显着提高其可靠性和可扩展性。首先，将挖掘出各种软件工程工件，以进行错误修复，（手动）重构（语义传播源代码源程序转换），并在有效执行命令式DL代码方面错过了机会。然后，对自动的分析和重构（i）迁移遗产，将执行式DL代码递延为更强大的命令式DL代码，以及（ii）指定其其他急切执行的势在果的DL代码应在运行时以图表的形式可靠，有效地执行。最后，将设计用于检测性能瓶颈和语义错误与基于图形的命令执行，否则热切执行的DL代码相关的新颖分析。这项贡献很重要，因为它填充了有效发展的技术，方法和工具的空隙，并不断发展，可信赖和高效的DL系统，这些系统普遍地使用了势在必行的和对象的DL编程。该奖项奖均反映了NSF的法定任务，并通过评估了基金会的范围，并通过评估了基金会的范围，并已被认为是值得一提的。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study

DOI：
10.1145/3524842.3528455
发表时间：
2022-01
期刊：
2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR)
影响因子：
0
作者：
Tatiana Castro V'elez;Raffi Khatchadourian;M. Bagherzadeh;A. Raja
通讯作者：
Tatiana Castro V'elez;Raffi Khatchadourian;M. Bagherzadeh;A. Raja

How many mutex bugs can a simple analysis find in Go programs?

简单分析一下Go程序中可以发现多少互斥量bug？

DOI：
发表时间：
2022
期刊：
Annual Conference of the Japanese Society for Software Science and Technology
影响因子：
0
作者：
Fumi Takeuchi. Hidehiko Masuhara. Raffi Khatchadourian, Youyou Cong
通讯作者：
Fumi Takeuchi. Hidehiko Masuhara. Raffi Khatchadourian, Youyou Cong

A Tool for Rejuvenating Feature Logging Levels via Git Histories and Degree of Interest

通过 Git 历史记录和兴趣程度恢复功能日志级别的工具