CAREER: A Dynamic Program Monitoring Framework Using Neural Network Hardware Grant uri icon


  • 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.

date/time interval

  • 2018 - 2022