no code implementations • 27 Dec 2024 • Ryan Raikman, Eric A. Moreno, Katya Govorkova, Siddharth Soni, Ethan Marx, William Benoit, Alec Gunny, Deep Chatterjee, Christina Reissel, Malina M. Desai, Rafia Omer, Muhammed Saleem, Philip Harris, Erik Katsavounidis, Michael W. Coughlin, Dylan Rankin
This paper presents the results of a Neural Network (NN)-based search for short-duration gravitational-wave transients in data from the third observing run of LIGO, Virgo, and KAGRA.
1 code implementation • 15 Nov 2024 • Ho Fung Tsoi, Dylan Rankin, Cecile Caillol, Miles Cranmer, Sridhara Dasu, Javier Duarte, Philip Harris, Elliot Lipeles, Vladimir Loncar
We introduce SymbolFit, a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data, while simultaneously providing uncertainty estimates in a single run.
no code implementations • 17 Oct 2024 • Liam Rankin Sheldon, Dylan Sheldon Rankin, Philip Harris
The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training.
no code implementations • 8 Sep 2024 • Zhixing Jiang, Dennis Yin, Yihui Chen, Elham E Khoda, Scott Hauck, Shih-Chieh Hsu, Ekaterina Govorkova, Philip Harris, Vladimir Loncar, Eric A. Moreno
This study presents an efficient implementation of transformer architectures in Field-Programmable Gate Arrays(FPGAs) using hls4ml.
no code implementations • 26 Jul 2024 • Deep Chatterjee, Ethan Marx, William Benoit, Ravi Kumar, Malina Desai, Ekaterina Govorkova, Alec Gunny, Eric Moreno, Rafia Omer, Ryan Raikman, Muhammed Saleem, Shrey Aggarwal, Michael W. Coughlin, Philip Harris, Erik Katsavounidis
The focus of AMPLFI is to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search, Aframe.
no code implementations • 27 Jun 2024 • Tommaso Baldi, Javier Campos, Ben Hawks, Jennifer Ngadiuba, Nhan Tran, Daniel Diaz, Javier Duarte, Ryan Kastner, Andres Meza, Melissa Quinnan, Olivia Weng, Caleb Geniesse, Amir Gholami, Michael W. Mahoney, Vladimir Loncar, Philip Harris, Joshua Agar, Shuyu Qin
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing.
no code implementations • 11 Mar 2024 • Philip Harris, Michael Kagan, Jeffrey Krupa, Benedikt Maier, Nathaniel Woodward
Self-Supervised Learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a variety of downstream tasks.
no code implementations • 1 Feb 2024 • Zhixing Jiang, Dennis Yin, Elham E Khoda, Vladimir Loncar, Ekaterina Govorkova, Eric Moreno, Philip Harris, Scott Hauck, Shih-Chieh Hsu
This work introduces a highly efficient implementation of the transformer architecture on a Field-Programmable Gate Array (FPGA) by using the \texttt{hls4ml} tool.
1 code implementation • 18 Jan 2024 • Ho Fung Tsoi, Vladimir Loncar, Sridhara Dasu, Philip Harris
In this work, we propose $\tt{SymbolNet}$, a neural network approach to symbolic regression specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs.
no code implementations • 9 Oct 2023 • Adrian Alan Pol, Ekaterina Govorkova, Sonja Gronroos, Nadezda Chernyavskaya, Philip Harris, Maurizio Pierini, Isobel Ojalvo, Peter Elmer
Unsupervised deep learning techniques are widely used to identify anomalous behaviour.
no code implementations • 6 May 2023 • Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar, Ekaterina Govorkova, Miles Cranmer, Sridhara Dasu, Peter Elmer, Philip Harris, Isobel Ojalvo, Maurizio Pierini
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints.
no code implementations • 9 Dec 2022 • Javier Duarte, Haoyang Li, Avik Roy, Ruike Zhu, E. A. Huerta, Daniel Diaz, Philip Harris, Raghav Kansal, Daniel S. Katz, Ishaan H. Kavoori, Volodymyr V. Kindratenko, Farouk Mokhtar, Mark S. Neubauer, Sang Eon Park, Melissa Quinnan, Roger Rusack, Zhizhen Zhao
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery.
no code implementations • 30 Sep 2022 • E. A. Huerta, Ben Blaiszik, L. Catherine Brinson, Kristofer E. Bouchard, Daniel Diaz, Caterina Doglioni, Javier M. Duarte, Murali Emani, Ian Foster, Geoffrey Fox, Philip Harris, Lukas Heinrich, Shantenu Jha, Daniel S. Katz, Volodymyr Kindratenko, Christine R. Kirkpatrick, Kati Lassila-Perini, Ravi K. Madduri, Mark S. Neubauer, Fotis E. Psomopoulos, Avik Roy, Oliver Rübel, Zhizhen Zhao, Ruike Zhu
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
no code implementations • 10 Aug 2022 • Sang Eon Park, Philip Harris, Bryan Ostdiek
In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic spaces.
no code implementations • 1 Jul 2022 • Elham E Khoda, Dylan Rankin, Rafael Teixeira de Lima, Philip Harris, Scott Hauck, Shih-Chieh Hsu, Michael Kagan, Vladimir Loncar, Chaitanya Paikara, Richa Rao, Sioni Summers, Caterina Vernieri, Aaron Wang
Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted.
no code implementations • 16 May 2022 • Nicolò Ghielmetti, Vladimir Loncar, Maurizio Pierini, Marcel Roed, Sioni Summers, Thea Aarrestad, Christoffer Petersson, Hampus Linander, Jennifer Ngadiuba, Kelvin Lin, Philip Harris
In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving.
no code implementations • 30 Mar 2022 • Philip Harris, Erik Katsavounidis, William Patrick McCormack, Dylan Rankin, Yongbin Feng, Abhijith Gandrakota, Christian Herwig, Burt Holzman, Kevin Pedro, Nhan Tran, Tingjun Yang, Jennifer Ngadiuba, Michael Coughlin, Scott Hauck, Shih-Chieh Hsu, Elham E Khoda, Deming Chen, Mark Neubauer, Javier Duarte, Georgia Karagiorgi, Mia Liu
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges.
no code implementations • 25 Oct 2021 • Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bahr, Jurgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomas E. Muller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan Rankin, Simon Rothman, ASHISH SHARMA, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
no code implementations • 4 Aug 2021 • Yifan Chen, E. A. Huerta, Javier Duarte, Philip Harris, Daniel S. Katz, Mark S. Neubauer, Daniel Diaz, Farouk Mokhtar, Raghav Kansal, Sang Eon Park, Volodymyr V. Kindratenko, Zhizhen Zhao, Roger Rusack
To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models.
no code implementations • 4 May 2021 • Giuseppe Di Guglielmo, Farah Fahim, Christian Herwig, Manuel Blanco Valentin, Javier Duarte, Cristian Gingu, Philip Harris, James Hirschauer, Martin Kwok, Vladimir Loncar, Yingyi Luo, Llovizna Miranda, Jennifer Ngadiuba, Daniel Noonan, Seda Ogrenci-Memik, Maurizio Pierini, Sioni Summers, Nhan Tran
We demonstrate that a neural network autoencoder model can be implemented in a radiation tolerant ASIC to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile.
2 code implementations • 9 Mar 2021 • Farah Fahim, Benjamin Hawks, Christian Herwig, James Hirschauer, Sergo Jindariani, Nhan Tran, Luca P. Carloni, Giuseppe Di Guglielmo, Philip Harris, Jeffrey Krupa, Dylan Rankin, Manuel Blanco Valentin, Josiah Hester, Yingyi Luo, John Mamish, Seda Orgrenci-Memik, Thea Aarrestad, Hamza Javed, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Sioni Summers, Javier Duarte, Scott Hauck, Shih-Chieh Hsu, Jennifer Ngadiuba, Mia Liu, Duc Hoang, Edward Kreinar, Zhenbin Wu
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains.
2 code implementations • 13 Jan 2021 • Thea Aarrestad, Vladimir Loncar, Nicolò Ghielmetti, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Christoffer Petersson, Hampus Linander, Yutaro Iiyama, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Kevin Pedro, Nhan Tran, Mia Liu, Edward Kreinar, Zhenbin Wu, Duc Hoang
We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs.
1 code implementation • 30 Nov 2020 • Aneesh Heintz, Vesal Razavimaleki, Javier Duarte, Gage DeZoort, Isobel Ojalvo, Savannah Thais, Markus Atkinson, Mark Neubauer, Lindsey Gray, Sergo Jindariani, Nhan Tran, Philip Harris, Dylan Rankin, Thea Aarrestad, Vladimir Loncar, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Mia Liu, Edward Kreinar, Zhenbin Wu
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks.
2 code implementations • 16 Oct 2020 • Dylan Sheldon Rankin, Jeffrey Krupa, Philip Harris, Maria Acosta Flechas, Burt Holzman, Thomas Klijnsma, Kevin Pedro, Nhan Tran, Scott Hauck, Shih-Chieh Hsu, Matthew Trahms, Kelvin Lin, Yu Lou, Ta-Wei Ho, Javier Duarte, Mia Liu
Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years.
Computational Physics Distributed, Parallel, and Cluster Computing High Energy Physics - Experiment Data Analysis, Statistics and Probability Instrumentation and Detectors
no code implementations • 8 Aug 2020 • Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler, Vladimir Loncar, Maurizio Pierini, Shah Rukh Qasim, Marcel Rieger, Sioni Summers, Gerrit Van Onsem, Kinga Wozniak, Jennifer Ngadiuba, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Zhenbin Wu
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering.
2 code implementations • 11 Mar 2020 • Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Duc Hoang, Sergo Jindariani, Edward Kreinar, Mia Liu, Vladimir Loncar, Jennifer Ngadiuba, Kevin Pedro, Maurizio Pierini, Dylan Rankin, Sheila Sagear, Sioni Summers, Nhan Tran, Zhenbin Wu
We discuss the trade-off between model accuracy and resource consumption.
3 code implementations • 5 Feb 2020 • Sioni Summers, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Duc Hoang, Sergo Jindariani, Edward Kreinar, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Dylan Rankin, Nhan Tran, Zhenbin Wu
We describe the implementation of Boosted Decision Trees in the hls4ml library, which allows the translation of a trained model into FPGA firmware through an automated conversion process.
1 code implementation • 18 Apr 2019 • Javier Duarte, Philip Harris, Scott Hauck, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Suffian Khan, Benjamin Kreis, Brian Lee, Mia Liu, Vladimir Lončar, Jennifer Ngadiuba, Kevin Pedro, Brandon Perez, Maurizio Pierini, Dylan Rankin, Nhan Tran, Matthew Trahms, Aristeidis Tsaris, Colin Versteeg, Ted W. Way, Dustin Werran, Zhenbin Wu
New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains.
Data Analysis, Statistics and Probability High Energy Physics - Experiment Computational Physics Instrumentation and Detectors
2 code implementations • 16 Apr 2018 • Javier Duarte, Song Han, Philip Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Jennifer Ngadiuba, Maurizio Pierini, Ryan Rivera, Nhan Tran, Zhenbin Wu
For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.