Open AccessPosted Content
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
Aneesh Heintz,Lindsey Gray,Nhan Tran,Savannah Thais,Gage Dezoort,Maurizio Pierini,Vesal Razavimaleki,Edward Kreinar,Mia Liu,Thea Klaeboe Aarrestad,Dylan Rankin,Jennifer Ngadiuba,Isobel Ojalvo,Mark Neubauer,Javier Duarte,Zhenbin Wu,Sioni Summers,Philip Harris,Sergo Jindariani,Markus Atkinson,Vladimir Loncar +20 more
TLDR
A considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.Abstract:
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for neural network to firmware conversion. We evaluate and compare the resource usage, latency, and tracking performance of our implementations based on a benchmark dataset. We find a considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.read more
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Graph Neural Networks for Particle Tracking and Reconstruction
Javier Duarte,Jean-Roch Vlimant +1 more
TL;DR: This chapter recapitulate the mathematical formalism of GNNs and highlight aspects to consider when designing these networks for HEP data, including graph construction, model architectures, learning objectives, and graph pooling.
Journal ArticleDOI
MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks
TL;DR: In this paper, an end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural network optimized using a multi-task objective on simulated events is presented.
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Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster
Jason St John,Christian Herwig,Diana Kafkes,William Pellico,Gabriel Perdue,Andres Quintero-Parra,Brian Schupbach,Kiyomi Seiya,Nhan Tran,Javier Duarte,Yunzhi Huang,Malachi Schram,Rachael Keller +12 more
TL;DR: Preliminary results by training a surrogate machine-learning model on real accelerator data to emulate the Booster environment and using this surrogate model to train the neural network for its regulation task are demonstrated.
Journal ArticleDOI
Performance of a geometric deep learning pipeline for HL-LHC particle tracking
Xiangyang Ju,Daniel Murnane,P. Calafiura,Nicholas Choma,Sean Conlon,Steven Farrell,Yaoyuan Xu,Maria Spiropulu,Jean-Roch Vlimant,Adam Aurisano,J. Hewes,Giuseppe Benedetto Cerati,Lindsey Gray,Thomas Klijnsma,Jim Kowalkowski,Markus Atkinson,Mark Neubauer,Gage Dezoort,Savannah Thais,Aditi Chauhan,Alex Schuy,Shih-Chieh Hsu,Alex Ballow,Alina Lazar +23 more
TL;DR: The Exa.TrkX project as mentioned in this paper applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking and achieved tracking efficiency and purity similar to production tracking algorithms.
Journal ArticleDOI
Recent advances in utility of artificial intelligence towards multiscale colloidal based materials design
TL;DR: A review of supervised and unsupervised strategies for colloidal material design can be found in this paper , where a collection of computer approaches ranging from quantum chemistry to molecular dynamics and continuum modeling are discussed.
References
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Proceedings Article
Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair,Geoffrey E. Hinton +1 more
TL;DR: Restricted Boltzmann machines were developed using binary stochastic hidden units that learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset.
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TL;DR: This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.
Proceedings Article
Deep Sparse Rectifier Neural Networks
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Journal ArticleDOI
Design of ion-implanted MOSFET's with very small physical dimensions
TL;DR: This paper considers the design, fabrication, and characterization of very small Mosfet switching devices suitable for digital integrated circuits, using dimensions of the order of 1 /spl mu/.
Posted Content
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia,Jessica B. Hamrick,Victor Bapst,Alvaro Sanchez-Gonzalez,Vinicius Zambaldi,Mateusz Malinowski,Andrea Tacchetti,David Raposo,Adam Santoro,Ryan Faulkner,Caglar Gulcehre,H. Francis Song,Andrew J. Ballard,Justin Gilmer,George E. Dahl,Ashish Vaswani,Kelsey R. Allen,Charlie Nash,Victoria Langston,Chris Dyer,Nicolas Heess,Daan Wierstra,Pushmeet Kohli,Matthew Botvinick,Oriol Vinyals,Yujia Li,Razvan Pascanu +26 more
TL;DR: It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective.
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