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 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.. Authors: Chae, Junghoon [ORNL] (ORCID:0000000206016746); Lim, Seung-Hwan [ORNL] (ORCID:0000000194616866); Kulkarni, Shruti [ORNL] (ORCID:0000000168949851); Schuman, Catherine D [University of Tennessee, Knoxville (UTK)]. DOE Contract: AC05-00OR22725