ASAML Project
ASAML is a group funded by an NSF HDR focused on applying accelerated machine learning to two big data science experiements: The Large Hadron Collider (LHC) and The Laser Interferometer Gravitational-Wave Observatory . The goal for this work is to both apply machine learning and hardware based machine learning acceleration to enhance the speed and quality of detector reconstruction. This work is affiliated with the large Fast Machine Learning lab and the NSF CSSI.
Shih-Chieh Hsu
Associate Professor of Physics
My research interests include search for physics beyond the Standard Model, and Machine Learning.
Publications
FPGAs-as-a-Service Toolkit (FaaST)
Heterogeneous computing has the potential for significant gains over traditional computing models. This work represents the first open-source FPGAs-as-a-service toolkit.
GPU coprocessors as a service for deep learning inference in high energy physics
Graphics Processing Units can accelerate algorithms to resolve tremendously growing demands for computing in large scientific experiments. We present a comprehensive exploration of the use of GPU-based hardware acceleration for deep learning inference within the data reconstruction workflow of high energy physics.
