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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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Atlanta, GA | Posted: February 2, 2018
Jackie Nemeth
School of Electrical and Computer Engineering
404-489-2906
Summary Sentence:
ECE Assistant Professor Tushar Krishna has been selected for a National Science Foundation (NSF) CISE Research Initiation Initiative Award.
Full Summary:
ECE Assistant Professor Tushar Krishna has been selected for a National Science Foundation (NSF) CISE Research Initiation Initiative Award.
Tushar KrishnaTushar Krishna has been selected for a National Science Foundation (NSF) CISE Research Initiation Initiative Award.
Krishna is an assistant professor in the Georgia Tech School of Electrical and Computer Engineering (ECE), where he leads the Synergy Lab. This two-year-long NSF award will support his project entitled “Enabling Neuroevolution in Hardware.”
Over the past few years, machine learning algorithms, especially neural networks (NN) have seen a surge of popularity owing to their potential in solving a wide variety of complex problems across image classification and speech recognition. Unfortunately, in order to be effective, NNs need to have the appropriate topology (connections between neurons) for the task at hand and have the right weights on the connections. This is known as supervised learning and requires training the NN by running it through terabytes to petabytes of data.
This form of machine learning is infeasible for the emerging domain of autonomous systems (robots/drones/cars) which will often operate in environments where the right topology for the task may be unknown or is constantly changing, and robust training data is not available. Autonomous systems need the ability to mirror human-like learning, where we are continuously learning, and often from experiences rather than being explicitly trained. This is known as reinforcement learning.
The focus of Krishna’s research will be on neuroevolution (NE), a class of reinforcement learning algorithms that evolve NN topologies and weights for the task at hand using evolutionary algorithms. The goal of this project will be on enabling NE in energy-constrained autonomous devices by leveraging opportunities for parallelism and hardware acceleration. If successful, this research could enable mass proliferation of autonomous drones and robots that can learn to perform tasks without being explicitly trained.
