Speaker
Description
Real-time data collection and analysis in large experimental facilities pose significant challenges across multiple domains, including high-energy physics, nuclear physics, and cosmology. Machine learning (ML)-based methods for real-time data compression have garnered substantial attention as a solution. In this talk, we will explore the use of deep neural networks in designing fast compression algorithms for 3D tensor data from the Time-Projection Chamber (TPC) at the sPHENIX experiment. Specifically, we will delve into the application of Bicephalous Convolutional Neural Networks designed to handle the sparsity and discontinuity of data from the tracking detector. Additionally, we will present our recent development in utilizing sparse convolution techniques to better exploit the data's inherent sparsity. Finally, we will briefly discuss several AI hardware accelerators that we have tested to achieve high-throughput inference.
