Fast inference of deep neural networks in FPGAs for particle physics. - Javier M. Duarte; Song Han 003; Philip C. Harris; Sergo Jindariani; Edward Kreinar; Benjamin Kreis; Jennifer Ngadiuba; Maurizio Pierini; Ryan A. Rivera; Nhan Tran; Zhenbin Wu (CoRR, 2018)

Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications. - Javier M. Duarte; Song Han 003; Philip C. Harris; Sergo Jindariani; Edward Kreinar; Benjamin Kreis; Vladimir Loncar; Jennifer Ngadiuba; Maurizio Pierini; Dylan S. Rankin; Ryan A. Rivera; Sioni Summers; et al. (FPGA, 2019)

Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan". - James F. Amundson; James Annis; Camille Avestruz; D. Bowring; João Caldeira; Giuseppe Cerati; Chihway L. Chang; Scott Dodelson; D. Elvira; A. Farahi; Krzysztof L. Genser; Lindsey Gray; et al. (CoRR, 2019)

FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing. - Javier M. Duarte; Philip C. Harris; Scott Hauck; Burt Holzman; Shih-Chieh Hsu; Sergo Jindariani; Suffian Khan; Benjamin Kreis; Brian Lee; Mia Liu; Vladimir Loncar; Jennifer Ngadiuba; et al. (Comput. Softw. Big Sci., 2019)

AIgean: An Open Framework for Machine Learning on Heterogeneous Clusters. - Naif Tarafdar; Giuseppe Di Guglielmo; Philip C. Harris; Jeffrey D. Krupa; Vladimir Loncar; Dylan S. Rankin; Nhan Tran; Zhenbin Wu; Qianfeng Shen; Paul Chow (FCCM, 2020)

FPGAs-as-a-Service Toolkit (FaaST). - Dylan S. Rankin; Jeffrey D. Krupa; Philip C. Harris; Maria Acosta Flechas; Burt Holzman; Thomas Klijnsma; Kevin Pedro; Nhan Tran; Scott Hauck; Shih-Chieh Hsu; Matthew Trahms; Kelvin Lin; et al. (H2RC@SC, 2020)

Fast inference of Boosted Decision Trees in FPGAs for particle physics. - Sioni Summers; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Duc Hoang; Sergo Jindariani; Edward Kreinar; Vladimir Loncar; Jennifer Ngadiuba; Maurizio Pierini; Dylan S. Rankin; Nhan Tran; et al. (CoRR, 2020)

GPU coprocessors as a service for deep learning inference in high energy physics. - Jeffrey D. Krupa; Kelvin Lin; Maria Acosta Flechas; Jack Dinsmore; Javier M. Duarte; Philip C. Harris; Scott Hauck; Burt Holzman; Shih-Chieh Hsu; Thomas Klijnsma; Mia Liu; Kevin Pedro; et al. (CoRR, 2020)

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs. - Aneesh Heintz; Vesal Razavimaleki; Javier M. Duarte; Gage DeZoort; Isobel Ojalvo; Savannah Thais; Markus Atkinson; Mark S. Neubauer; Lindsey Gray; Sergo Jindariani; Nhan Tran; Philip C. Harris; et al. (CoRR, 2020)

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics. - Yutaro Iiyama; Gianluca Cerminara; Abhijay Gupta; Jan Kieseler; Vladimir Loncar; Maurizio Pierini; Shah Rukh Qasim; Marcel Rieger; Sioni Summers; Gerrit Van Onsem; Kinga Anna Wozniak; Jennifer Ngadiuba; et al. (Frontiers Big Data, 2020)

GPU-Accelerated Machine Learning Inference as a Service for Computing in Neutrino Experiments. - Michael Wang 003; Tingjun Yang; Maria Acosta Flechas; Philip C. Harris; Benjamin Hawks; Burt Holzman; Kyle Knoepfel; Jeffrey D. Krupa; Kevin Pedro; Nhan Tran (Frontiers Big Data, 2020)

Fast convolutional neural networks on FPGAs with hls4ml. - Thea Aarrestad; Vladimir Loncar; Maurizio Pierini; Sioni Summers; Jennifer Ngadiuba; Christoffer Petersson; Hampus Linander; Yutaro Iiyama; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Dylan S. Rankin; et al. (CoRR, 2021)

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices. - Farah Fahim; Benjamin Hawks; Christian Herwig; James Hirschauer; Sergo Jindariani; Nhan Tran; Luca P. Carloni; Giuseppe Di Guglielmo; Philip C. Harris; Jeffrey D. Krupa; Dylan S. Rankin; Manuel Blanco Valentin; et al. (CoRR, 2021)

A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC. - Giuseppe Di Guglielmo; Farah Fahim; Christian Herwig; Manuel Blanco Valentin; Javier M. Duarte; Cristian Gingu; Philip C. Harris; James Hirschauer; Martin Kwok; Vladimir Loncar; Yingyi Luo; Llovizna Miranda; et al. (CoRR, 2021)

A FAIR and AI-ready Higgs Boson Decay Dataset. - Yifan Chen; Eliu A. Huerta; Javier M. Duarte; Philip C. Harris; Daniel S. Katz; Mark S. Neubauer; Daniel Diaz 003; Farouk Mokhtar; Raghav Kansal; Sang Eon Park; Volodymyr V. Kindratenko; Zhizhen Zhao 001; et al. (CoRR, 2021)

Applications and Techniques for Fast Machine Learning in Science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci Memik; et al. (CoRR, 2021)

Fast convolutional neural networks on FPGAs with hls4ml. - Thea Aarrestad; Vladimir Loncar; Nicolò Ghielmetti; Maurizio Pierini; Sioni Summers; Jennifer Ngadiuba; Christoffer Petersson; Hampus Linander; Yutaro Iiyama; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; et al. (Mach. Learn. Sci. Technol., 2021)

GPU coprocessors as a service for deep learning inference in high energy physics. - Jeffrey D. Krupa; Kelvin Lin; Maria Acosta Flechas; Jack Dinsmore; Javier M. Duarte; Philip C. Harris; Scott Hauck; Burt Holzman; Shih-Chieh Hsu; Thomas Klijnsma; Mia Liu; Kevin Pedro; et al. (Mach. Learn. Sci. Technol., 2021)

Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml. - Jennifer Ngadiuba; Vladimir Loncar; Maurizio Pierini; Sioni Summers; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Dylan S. Rankin; Sergo Jindariani; Mia Liu; Kevin Pedro; Nhan Tran; et al. (Mach. Learn. Sci. Technol., 2021)

A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics. - Alec Gunny; Dylan S. Rankin; Philip C. Harris; Erik Katsavounidis; Ethan Marx; Muhammed Saleem; Michael Coughlin; William Benoit (FlexScience@HPDC, 2022)

Physics Community Needs, Tools, and Resources for Machine Learning. - Philip C. Harris; Erik Katsavounidis; William Patrick McCormack; Dylan S. Rankin; Yongbin Feng; Abhijith Gandrakota; Christian Herwig; Burt Holzman; Kevin Pedro; Nhan Tran; Tingjun Yang; Jennifer Ngadiuba; et al. (CoRR, 2022)

Data Science and Machine Learning in Education. - Gabriele Benelli; Thomas Y. Chen; Javier M. Duarte; Matthew Feickert; Matthew J. Graham; Lindsey Gray; Dan Hackett; Philip C. Harris; Shih-Chieh Hsu; Gregor Kasieczka; Elham E Khoda; Matthias Komm; et al. (CoRR, 2022)

Neural Embedding: Learning the Embedding of the Manifold of Physics Data. - Sang Eon Park; Philip C. Harris; Bryan Ostdiek (CoRR, 2022)

FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective. - Eliu A. Huerta; Ben Blaiszik; L. Catherine Brinson; Kristofer E. Bouchard; Daniel Diaz 003; Caterina Doglioni; Javier M. Duarte; Murali Emani; Ian T. Foster; Geoffrey C. Fox; Philip C. Harris; Lukas Heinrich; et al. (CoRR, 2022)

Applications and Techniques for Fast Machine Learning in Science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci Memik; et al. (Frontiers Big Data, 2022)

AIgean: An Open Framework for Deploying Machine Learning on Heterogeneous Clusters. - Naif Tarafdar; Giuseppe Di Guglielmo; Philip C. Harris; Jeffrey D. Krupa; Vladimir Loncar; Dylan S. Rankin; Nhan Tran; Zhenbin Wu; Qianfeng Shen; Paul Chow (ACM Trans. Reconfigurable Technol. Syst., 2022)

Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml. - Nicolò Ghielmetti; Vladimir Loncar; Maurizio Pierini; Marcel Roed; Sioni Summers; Thea Aarrestad; Christoffer Petersson; Hampus Linander; Jennifer Ngadiuba; Kelvin Lin; Philip C. Harris (Mach. Learn. Sci. Technol., 2022)

Knowledge Distillation for Anomaly Detection. - Adrian Alan Pol; Ekaterina Govorkova; Sonja Grönroos; Nadezda Chernyavskaya; Philip C. Harris; Maurizio Pierini; Isobel Ojalvo; Peter Elmer (ESANN, 2023)

Symbolic Regression on FPGAs for Fast Machine Learning Inference. - Ho Fung Tsoi; Adrian Alan Pol; Vladimir Loncar; Ekaterina Govorkova; Miles D. Cranmer; Sridhara Dasu; Peter Elmer; Philip C. Harris; Isobel Ojalvo; Maurizio Pierini (CoRR, 2023)

Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings. - Deep Chatterjee; Philip C. Harris; Maanas Goel; Malina Desai; Michael W. Coughlin; Erik Katsavounidis (CoRR, 2023)

Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml. - Elham E Khoda; Dylan S. Rankin; Rafael Teixeira de Lima; Philip C. Harris; Scott Hauck; Shih-Chieh Hsu; Michael Kagan; Vladimir Loncar; Chaitanya Paikara; Richa Rao; Sioni Summers; Caterina Vernieri; et al. (Mach. Learn. Sci. Technol., 2023)

Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing. - Tejin Cai; Kenneth Herner; Tingjun Yang; Michael Wang 003; Maria Acosta Flechas; Philip C. Harris; Burt Holzman; Kevin Pedro; Nhan Tran (Comput. Softw. Big Sci., 2023)

FAIR AI models in high energy physics. - Javier M. Duarte; Haoyang Li; Avik Roy; Ruike Zhu; Eliu A. Huerta; Daniel Diaz 003; Philip C. Harris; Raghav Kansal; Daniel S. Katz; Ishaan H. Kavoori; Volodymyr V. Kindratenko; Farouk Mokhtar; et al. (Mach. Learn. Sci. Technol., 2023)

Reliable edge machine learning hardware for scientific applications. - Tommaso Baldi; Javier Campos; Benjamin Hawks; Jennifer Ngadiuba; Nhan Tran; Daniel Diaz 003; Javier M. Duarte; Ryan Kastner; Andres Meza 001; Melissa Quinnan; Olivia Weng; Caleb Geniesse; et al. (VTS, 2024)

SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning. - Ho Fung Tsoi; Vladimir Loncar; Sridhara Dasu; Philip C. Harris (CoRR, 2024)

Ultra Fast Transformers on FPGAs for Particle Physics Experiments. - Zhixing Jiang; Dennis Yin; Elham E Khoda; Vladimir Loncar; Ekaterina Govorkova; Eric A. Moreno; Philip C. Harris; Scott Hauck; Shih-Chieh Hsu (CoRR, 2024)

Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models. - Philip C. Harris; Michael Kagan; Jeffrey D. Krupa; Benedikt Maier; Nathaniel Woodward (CoRR, 2024)

Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml. - Zhixing Jiang; Dennis Yin; Yihui Chen; Elham E Khoda; Scott Hauck; Shih-Chieh Hsu; Ekaterina Govorkova; Philip C. Harris; Vladimir Loncar; Eric A. Moreno (CoRR, 2024)

MACK: Mismodeling Addressed with Contrastive Knowledge. - Liam Rankin Sheldon; Dylan Sheldon Rankin; Philip C. Harris (CoRR, 2024)

A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run. - Ryan Raikman; Eric A. Moreno; Katya Govorkova; Siddharth Soni; Ethan Marx; William Benoit; Alec Gunny; Deep Chatterjee; Christina Reissel; Malina Desai; Rafia Omer; Muhammed Saleem; et al. (CoRR, 2024)

Corrigendum: Applications and techniques for fast machine learning in science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci-Memik; et al. (Frontiers Big Data, 2024)

Rapid likelihood free inference of compact binary coalescences using accelerated hardware. - Deep Chatterjee; Ethan Marx; William Benoit; Ravi Kumar; Malina Desai; Ekaterina Govorkova; Alec Gunny; Eric A. Moreno; Rafia Omer; Ryan Raikman; Muhammed Saleem; Shrey Aggarwal; et al. (Mach. Learn. Sci. Technol., 2024)

GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders. - Ryan Raikman; Eric A. Moreno; Ekaterina Govorkova; Ethan Marx; Alec Gunny; William Benoit; Deep Chatterjee; Rafia Omer; Muhammed Saleem; Dylan S. Rankin; Michael W. Coughlin; Philip C. Harris; et al. (Mach. Learn. Sci. Technol., 2024)

SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression. - Ho Fung Tsoi; Vladimir Loncar; Sridhara Dasu; Philip C. Harris (Mach. Learn. Sci. Technol., 2025)

Building Machine Learning Challenges for Anomaly Detection in Science. - Elizabeth G. Campolongo; Yuan-Tang Chou; Ekaterina Govorkova; Wahid Bhimji; Wei-Lun Chao; Chris Harris; Shih-Chieh Hsu; Hilmar Lapp; Mark S. Neubauer; Josephine M. Namayanja; Aneesh Subramanian; Philip C. Harris; et al. (CoRR, 2025)

SymbolFit: Automatic Parametric Modeling with Symbolic Regression. - Ho Fung Tsoi; Dylan S. Rankin; Cecile Caillol; Miles D. Cranmer; Sridhara Dasu; Javier M. Duarte; Philip C. Harris; Elliot Lipeles; Vladimir Loncar (Comput. Softw. Big Sci., 2025)