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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: Overcoming Noise and Variations in Low-precision Neural Networks
Committee:
Dr. David Anderson, ECE, Chair , Advisor
Dr. Arijit Raychowdhury, ECE
Dr. Aaron Lanterman, ECE
Dr. Shaolan Li, ECE
Dr. Hyesoon Kim, CoC
Abstract: Traditional machine learning algorithms and neural networks are implemented using powerful digital computational architectures such as GPUs, TPUs, and FPGAs, demonstrating high performance and successfully completing previously impossible tasks. Unfortunately, the power required to train and generate predictions with the neural networks is too high to be implemented in energy-constrained systems such as implants and edge devices. Many of these systems would significantly benefit from on-board neural networks that could respond to stimuli in real time. The important question that this work seeks to address is how to bring the game-changing power of neural networks closer to the edge of the internet of things without significant degradation of performance or battery life.
