SNNVis: Visualizing Graph Embedding of Evolutionary Optimization for Spiking Neural Networks

While Spiking Neural Networks (SNNs) show a lot of promise, it is difficult to optimize them because applying traditional gradient-based optimization techniques is difficult. Even though evolutionary algorithms (EAs) have been shown to promise to optimize SNNs, understanding the relationship between...

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Published in:2024 International Conference on Neuromorphic Systems (ICONS) pp. 327 - 330
Main Authors: Chae, Junghoon, Lim, Seung-Hwan, Kulkarni, Shruti, Schuman, Catherine
Format: Conference Proceeding
Language:English
Published: IEEE 30.07.2024
Subjects:
evolutionary algorithm
Evolutionary computation
Neuromorphics
Optimization
SNN
Spiking neural networks
Visual analytics
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Abstract While Spiking Neural Networks (SNNs) show a lot of promise, it is difficult to optimize them because applying traditional gradient-based optimization techniques is difficult. Even though evolutionary algorithms (EAs) have been shown to promise to optimize SNNs, understanding the relationship between evolving the characteristics of SNNs and their performance to improve the optimization algorithm is challenging because of the complex characteristics and huge population size. We propose visual analytics with novel graph embedding for evolutionary SNNs to address the challenges. While existing graph embedding techniques have limitations in preserving the specific features of the nodes and edges, our approach maintains them. Also, we develop visual analytics for understanding the relationship between the network performance and the features of nodes and edges and exploring and analyzing the evolving SNNs to build insights into improving the EA.
AbstractList While Spiking Neural Networks (SNNs) show a lot of promise, it is difficult to optimize them because applying traditional gradient-based optimization techniques is difficult. Even though evolutionary algorithms (EAs) have been shown to promise to optimize SNNs, understanding the relationship between evolving the characteristics of SNNs and their performance to improve the optimization algorithm is challenging because of the complex characteristics and huge population size. We propose visual analytics with novel graph embedding for evolutionary SNNs to address the challenges. While existing graph embedding techniques have limitations in preserving the specific features of the nodes and edges, our approach maintains them. Also, we develop visual analytics for understanding the relationship between the network performance and the features of nodes and edges and exploring and analyzing the evolving SNNs to build insights into improving the EA.
Author Chae, Junghoon
Kulkarni, Shruti
Lim, Seung-Hwan
Schuman, Catherine
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  givenname: Junghoon
  surname: Chae
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  givenname: Seung-Hwan
  surname: Lim
  fullname: Lim, Seung-Hwan
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  givenname: Shruti
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  email: kulkarnisr@ornl.gov
  organization: Oak Ridge National Laboratory
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  givenname: Catherine
  surname: Schuman
  fullname: Schuman, Catherine
  email: cschuman@utk.edu
  organization: University of Tennessee
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Snippet While Spiking Neural Networks (SNNs) show a lot of promise, it is difficult to optimize them because applying traditional gradient-based optimization...
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StartPage 327
SubjectTerms evolutionary algorithm
Evolutionary computation
Neuromorphics
Optimization
SNN
Spiking neural networks
Visual analytics
Title SNNVis: Visualizing Graph Embedding of Evolutionary Optimization for Spiking Neural Networks
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