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

Fuente: arXiv

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Bibliographic Details
Main Authors:	Schulte, Jan-Frederik, Ramhorst, Benjamin, Sun, Chang, Mitrevski, Jovan, Ghielmetti, Nicolò, Lupi, Enrico, Danopoulos, Dimitrios, Loncar, Vladimir, Duarte, Javier, Burnette, David, Laatu, Lauri, Tzelepis, Stylianos, Axiotis, Konstantinos, Berthet, Quentin, Wang, Haoyan, White, Paul, Demirsoy, Suleyman, Colombo, Marco, Aarrestad, Thea, Summers, Sioni, Pierini, Maurizio, Di Guglielmo, Giuseppe, Ngadiuba, Jennifer, Campos, Javier, Hawks, Ben, Gandrakota, Abhijith, Fahim, Farah, Tran, Nhan, Constantinides, George, Que, Zhiqiang, Luk, Wayne, Tapper, Alexander, Hoang, Duc, Paladino, Noah, Harris, Philip, Lai, Bo-Cheng, Valentin, Manuel, Forelli, Ryan, Ogrenci, Seda, Gerlach, Lino, Flynn, Rian, Liu, Mia, Diaz, Daniel, Khoda, Elham, Quinnan, Melissa, Solares, Russell, Parajuli, Santosh, Neubauer, Mark, Herwig, Christian, Tsoi, Ho Fung, Rankin, Dylan, Hsu, Shih-Chieh, Hauck, Scott
Format:	Preprint
Published:	2025
Subjects:	Hardware Architecture Machine Learning High Energy Physics - Experiment
Online Access:	Acceder al recurso
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Internet

https://arxiv.org/abs/2512.01463