FastML Lab

Real-time and accelerated ML for fundamental sciences

Fast ML Lab is a research collective of physicists, engineers, and computer scientists interested in deploying machine learning algorithms for unique and challenging scientific applications. Our projects range from real-time, on-detector and low latency machine learning applications to high-throughput heterogeneous computing big data challenges. We are interested in deploying sophisticated machine learning algorithms to advance the exploration of fundamental physics from the world’s biggest colliders to the most intense particle beams to the cosmos.


We anticipate our next workshop to take place in spring 2022. Our virtual 2020 workshop was hosted by Southern Methodist University from November 30 - December 3, 2020, and presented new applications and results. Our first 2019 workshop was held at Fermilab.

CERN Courier

CERN Courier

An article in the CERN Courier about AI Triggers at CERN titled "Hunting anomalies with an AI trigger"

Symmetry Magazine

Symmetry Magazine

Symmetry Magazine article about fast AI trigger systems in particle physics titled "Blink and it's gone"

Tiny MLPerf

Tiny MLPerf

hls4ml team submits results for the first Tiny MLPerf Inference Benchmark

Nvidia Triton

Nvidia Triton

Scaling Inference in High Energy Particle Physics at Fermilab Using NVIDIA Triton Inference Server



hls4ml: an open-source code framework for translating machine learning algorithms directly into FPGA firmware



Zenuity has become the first automotive company to team up with CERN to develop ML for autonomous drive cars

Microsoft Azure

Microsoft Azure

Microsoft Azure collaboration deploying FPGAs to accelerate ML to prototype computing solutions for future big science experiments

Xilinx Case Study

Xilinx Case Study

A story from Xilinx on how we use high level synthesis to find the best events at the Large Hadron Collider



  • C. Herwig, An ML Control System for the Fermilab Booster, BIDS Machine Learning and Science Forum, April 2021, abstract

  • P. Harris, Quick and Quirk with Quarks, IAIFI Colloquium Online, March 2021, video

  • P. Harris, Scientific Applications of FPGAs at the LHC, ISFPGA 2021 (keynote), abstract

  • J. Duarte, AI at the Edge of Particle Physics, video

  • J. Duarte, hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices, tinyML Research Symposium 2021, video

  • D. Rankin, FPGAs-as-a-Service Toolkit (FaaST), Heterogeneous High-Performance Reconfigurable Computing Workshop at Supercomputing 2020, slides

  • P. Harris, ML Acceleration with Heterogeneous Computing for Big Data Physics Experiments, Heterogeneous High Performance Computing Workshop at Supercomputing 2019, slides

  • K. Pedro, FPGA-accelerated machine learning inference as a service for particle physics computing, CHEP 2019, slides

  • J. Duarte, Machine Learning on FPGAs for low latency and high throughput inference, eScience 2019, slides

  • M. Liu, FPGA-accelerated machine learning inference as a solution for particle physics computing challenges, PASC 2019, slides

  • J. Ngadiuba, hls4ml: deploying deep learning on FPGAs for trigger and data acquisition, ACAT 2019, slides

  • J. Duarte, FPGA-accelerated machine learning inference for particle physics computing challenges, CTD 2019, slides

  • J. Duarte, hls4ml: deploying deep learning on FPGAs for L1 trigger and data acquisition, TWEPP 2018, slides

  • J. Ngadiuba, Synthesizing machine learning algorithms on FPGAs, CHEP 2018, slides

  • N. Tran, Neural networks in FPGAs for trigger and DAQ, CTD 2018, slides



Jennifer Ngadiuba (PhD, Physics); 


Thea Årrestad (PhD, Physics);  Vladimir Loncar (PhD, Computer Science);  Maurizio Pierini (PhD, Physics);  Sioni Summers (PhD, Physics); 


Giuseppe Di Guglielmo (PhD, Computer Science); 


Lindsey Gray (PhD, Physics);  Christian Herwig (PhD, Physics);  Burt Holzman (PhD, Physics);  Sergo Jindariani (PhD, Physics);  Thomas Klijnsma (PhD, Physics);  Ben Kreis (PhD, Physics);  Kevin Pedro (PhD, Physics);  Ryan Rivera (PhD, EE);  Nhan Tran (PhD, Physics);  Mike Wang (PhD, Physics);  Tingjun Yang (PhD, Physics); 


EJ Kreinar ( Computer Science); 


Joshua Agar (PhD, Material Science and Engineering); 


Jack Dinsmore (Undergraduate, Physics);  Song Han (PhD, EECS);  Phil Harris (PhD, Physics);  Duc Hoang (Graduate, Physics);  Jeffrey Krupa (Graduate, Physics);  Eric Moreno (Graduate, Physics);  Noah Paladino (Graduate, Physics);  Sang Eon Park (Graduate, Physics);  Dylan Rankin (PhD, Physics); 


Farah Fahim (Adjunct, ECE);  Seda Memik-Ogrenci ( ECE);  Nhan Tran (Adjunct, ECE); 


Mia Liu (PhD, Physics); 


Daniel Diaz (PhD, Physics);  Javier Duarte (PhD, Physics);  Ryan Kastner (PhD, Computer Science);  Farouk Mokhtar (Graduate, Physics);  Tai Nguyen (Undergraduate, Physics);  Vesal Razavimaleki (Undergraduate, Engineering Physics);  Rushil Roy (Undergraduate, Electrical Engineering);  Olivia Weng (Graduate, Computer Science and Engineering); 


Zhenbin Wu (PhD, Physics); 


Markus Atkinson (PhD, Physics);  Mark Neubauer (PhD, Physics); 


Scott Hauck (PhD, EECS);  Shih-Chieh Hsu (PhD, Physics); 


We are a community that fosters knowledge transfer of accelerated and real-time artificial intelligence applications to fundamental science. By joining the community, you agree to abide by our codes of conduct and policies of collaboration.

Sponsors and Partners

We are graciously funded by the following organizations:

European Research Council Department of Energy National Science Foundation Internet 2 DARPA

And are collaborating with these organizations:

Institute for Research and Innovation in Software for High Energy Physics Institute for Accelerated AI Algorithms for Data-Driven Discovery