Matthew Joseph Adiletta

Matthew Adiletta, David Brooks, Gu-Yeon Wei

Graph Neural Networks (GNNs) are a class of neural networks designed to extract information from the graphical structure of data. Graph Convolutional Networks (GCNs) are a widely used type of GNN for transductive graph learning problems which apply convolution to learn information from graphs. GCN is a challenging algorithm from an architecture perspective due to inherent sparsity, low data reuse, and massive memory capacity requirements. Traditional neural algorithms exploit the high compute capacity of GPUs to achieve high performance for both inference and training. The architectural decision to use a GPU for GCN inference is a question explored in this work. GCN on both CPU and GPU was characterized in order to better understand the implications of graph size, embedding dimension, and sampling on performance.

Matthew Joseph Adiletta, Oliver Thomas

This project brings music to sight. Music can be a visual masterpiece. Some people naturally experience a visualization of audio - a condition called synesthesia. The type of synesthesia explored is when sounds create colors in the 'mind's eye.' Project included interviews with people who experience synesthesia, examination of prior art, and topic research to inform project design. Audio input, digital signal processing (including Fast Fourier Transforms (FFTs)) and data manipulation produce arguments required for our visualization. Arguments are then applied to a physics particle simulator which is re-purposed to model a synesthetic experience. The result of the project is a simulator in MAX 8, which generates a visual performance using particles by varying each particle's position, velocity, and color based on parameters extracted via digital processing of input audio.