*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Title: Self Adaptive Reconfigurable Arrays: ML-assisted HW Architectures for the Ubiquitous and Evolving AI Landscape
Committee:
Dr. Krishna, Advisor
Dr. Mukhopadhyay, Chair
Dr. Kim
Abstract: The objective of the proposed research is to create architectures, tool, models, and methodologies for building efficient accelerators, that are adaptable to the changing landscape of machine learning workloads and applications. The advent of new deep learning algorithms have transformed the world of computing as we know it. While in one hand these algorithms have engendered solutions to previously unsolvable problems in machine comprehension and data analysis, the huge compute demand for running these algorithms have created new exciting problems in systems and hardware design. Computer architects have taken bold strides in democratizing the power of these algorithms by creating high performance and energy efficient accelerators. However, as the landscape of deep neural networks continue to evolve so does the requirements of acceleration. Unfortunately designing accelerators is hard and expensive. This thesis proposes to address some of these problems by exploring architecture design, developing tools and analytical models, and finally by channeling the power of machine learning into the design process of machine learning accelerators.
