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AI and machine learning has become critical in every possible application in today's ecosystem. For example, most of the popular applications used today give you customized recommendations – for example, Youtube recommends personalized videos. Many of these applications have a recommendation system that learns to make these recommendations.
However, these applications generate a tremendous amount of data storing petabytes of data each day. This research project aims to look at AI and Recommendation applications and how to reduce storage requirements. The techniques could span from application logic, system software (i.e., OS), and computer architecture.
This project will extend software built by prior Aresty researchers and explore AI, operating systems (OS), computer architecture, data structures, and algorithms to develop system software for data placement across heterogeneous storage devices.
The project will be a joint collaboration between Rutgers and Samsung Memory Systems labs. The outcome of the project will aim to accelerate AI applications. In addition, the prospective research assistant will have an opportunity to contribute to research publications.
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