CAREER: A Dynamic Program Monitoring Framework Using Neural Network Hardware

职业：使用神经网络硬件的动态程序监控框架

• 批准号: 1652655
• 负责人: Abdullah Muzahid
• 金额: $ 44.99万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-15 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1652655&HistoricalAwards=false
• 关键词: CAREER; Dynamic; Program; Monitoring; Framework

Software bugs and security attacks cripple US economy by costing more than $150 billion a year. However, there has been no major innovation in this context. This research project aims to change that fact with the help of neural network based hardware. If the project is successful, it will significantly affect current industry practices and spur a new trend. It will encourage companies to invest in new techniques for debugging and security attack analysis using neural network hardware and make a compelling use case for the hardware implementation, thereby influencing continuous investment in neural network hardware. In addition, the project will contribute to the research and educational activities of a minority serving institution. Students will be tightly integrated into the project through dissertation, thesis work, and undergraduate research work. The PI will incorporate emerging architecture design and its programming in undergraduate and graduate coursework. Moreover, the PI will involve local high school students in computer science related projects through summer internships.Neural network is a machine learning technique that mimics human brain. Therefore, neural network hardware provides some unique capabilities that can be utilized in many different ways. This project proposes to utilize neural network hardware for "program monitoring". Program execution monitoring is often used to detect software bugs, performance issues, security attacks etc. Neural network hardware will learn the normal "behavior" of the program. Then it will detect any deviation of such behavior. Such deviation can be attributed to software bugs, performance issues or security attacks. The proposed approach provides a general framework for handling these issues. Due to online learning and testing capability of neural network hardware, the framework will be adaptive to any change in program inputs, code, and platforms.

软件漏洞和安全攻击削弱了美国经济，每年损失超过1500亿美元。然而，在这方面没有重大创新。该研究项目旨在借助基于神经网络的硬件来改变这一事实。如果该项目成功，将对当前的行业实践产生重大影响，并催生一种新的趋势。它将鼓励公司投资使用神经网络硬件进行调试和安全攻击分析的新技术，并为硬件实现提供引人注目的用例，从而影响对神经网络硬件的持续投资。此外，该项目还将促进一个少数民族服务机构的研究和教育活动。学生将通过论文，论文工作和本科研究工作紧密结合到项目中。PI将在本科和研究生课程中纳入新兴的建筑设计及其编程。此外，PI将通过暑期实习让本地高中生参与计算机科学相关的项目。神经网络是一种模仿人脑的机器学习技术。因此，神经网络硬件提供了一些独特的功能，可以以许多不同的方式使用。该项目提出利用神经网络硬件进行“程序监控”。程序执行监控通常用于检测软件错误，性能问题，安全攻击等。神经网络硬件将学习程序的正常“行为”。然后，它将检测这种行为的任何偏差。此类偏差可能归因于软件错误、性能问题或安全攻击。拟议的办法为处理这些问题提供了一个总体框架。由于神经网络硬件的在线学习和测试能力，该框架将适应程序输入、代码和平台的任何变化。

项目成果

Bugaroo: Exposing Memory Model Bugs in Many-Core Systems

Bugaroo：暴露多核系统中的内存模型错误

• DOI: 10.1109/issre.2018.00028
• 发表时间: 2018
• 期刊: IEEE 29th International Symposium on Software Reliability Engineering (ISSRE
• 作者: Islam, Mohammad Majharul;Muzahid, Abdullah
• 通讯作者: Muzahid, Abdullah

A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions

• 发表时间: 2017-09
• 作者: Mejbah Alam;Justin Emile Gottschlich;Nesime Tatbul;Javier Turek;T. Mattson;A. Muzahid