Mar 20 – 22, 2018
University of Washington Seattle
US/Pacific timezone

Neural Networks in FPGAs for Trigger and DAQ

Mar 20, 2018, 12:00 PM
Physics-Astronomy Auditorium A118 (University of Washington Seattle)

Physics-Astronomy Auditorium A118

University of Washington Seattle

Oral 3: Machine learning approaches Session1


Nhan Viet Tran (Fermi National Accelerator Lab. (US))


Machine learning methods are becoming ubiquitous across the LHC and particle physics. However, the exploration of such techniques within the field in low latency, low power FPGA hardware has only just begun. There is great potential to improve trigger and data acquisition performance, more generally for pattern recognition problems, and potentially beyond. We present a case study for using neural networks in FPGAs. Our study takes jet substructure as an example since it is a field familiar with machine learning, but lessons are far-reaching. We map out resource usage and latency versus types of machine learning algorithms and their hyper-parameters to identify the problems in particle physics that would benefit. We develop a package based on High Level Synthesis (HLS) to build network architectures which is readily accessible to a broad user base.

Primary authors

Nhan Viet Tran (Fermi National Accelerator Lab. (US)) Javier Mauricio Duarte (Fermi National Accelerator Lab. (US)) Ben Kreis (Fermi National Accelerator Lab. (US)) Jennifer Ngadiuba (CERN) Maurizio Pierini (CERN) Zhenbin Wu (University of Illinois at Chicago (US)) Edward Kreinar (Hawkeye 360) Song Han (Stanford University) Philip Coleman Harris (Massachusetts Inst. of Technology (US))

Presentation materials