activity
20152026
most citedFast inference of deep neural networks in FPGAs for particle physics

438 citations · 1.5k across the 39 of their papers we have counts for

collaborators
Showing 2025Show all

7 papers · 1 filter

cs.AR2025

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

Jan-Frederik Schulte, Benjamin Ramhorst, Chang Sun +50

We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can b…

hep-ph2025

Why Is Attention Sparse In Particle Transformer?

Timothy Legge, Aaron Wang, Jacob Ortiz +7

Transformer-based models have achieved state-of-the-art performance in jet tagging at the CERN Large Hadron Collider (LHC), with the Particle Transformer (ParT) representing a lead…

cs.LG2025

An Evaluation of Representation Learning Methods in Particle Physics Foundation Models

Michael Chen, Raghav Kansal, Abhijith Gandrakota +3

We present a systematic evaluation of representation learning objectives for particle physics within a unified framework. Our study employs a shared transformer-based particle-clou…

physics.ins-det2025

Sub-microsecond Transformers for Jet Tagging on FPGAs

Lauri Laatu, Chang Sun, Arianna Cox +7

We present the first sub-microsecond transformer implementation on an FPGA achieving competitive performance for state-of-the-art high-energy physics benchmarks. Transformers have…

cs.LG2025

Spatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging

Aaron Wang, Zihan Zhao, Subash Katel +6

Transformers are very effective in capturing both global and local correlations within high-energy particle collisions, but they present deployment challenges in high-data-throughp…

cs.AI2025★ 1 cited

The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)

Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97

This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…

physics.ins-det2025★ 3 cited

Fast Jet Tagging with MLP-Mixers on FPGAs

Chang Sun, Jennifer Ngadiuba, Maurizio Pierini +1

We explore the innovative use of MLP-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs. MLP-Mixers excel in process…