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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
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Title:
Energy-efficient Digital Hardware for System Identification of Integrated Circuit Systems
Committee:
Dr. Mukhopahdyay, Advisor
Dr. Raychowdhury, Chair
Dr. Yalamanchili
Abstract:
The objective of the proposed research is to propose energy-efficient hardware to perform system identification on complex systems, specifically a nonlinear dynamic system. To do this, we first analyze a frequency-domain system identification method of a simple linear thermal system; multi-input and multi-output (MIMO) system. This simple example demonstrates how much system identification is an important problem in engineering domain. Then, we extend the system to be estimated into a nonlinear system, especially image processing (classification) or temporal sequence mapping (power pattern-workload). The system identification of such nonlinear systems can be successfully done by neural networks (feedforward or recurrent). To design energy-efficient neuromorphic hardware, we analyze the impact of hardware-induced error on the performance (accuracy) of several neural networks in either learning or inference. This algorithmic analysis is expanded to demonstrate energy-efficient digital neuromorphic hardware.
