hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drastically improve experimental design and accelerate scien...

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Detalles Bibliográficos
Autores principales: Fahim, Farah, Hawks, Benjamin, Herwig, Christian, Hirschauer, James, Jindariani, Sergo, Tran, Nhan, Carloni, Luca P., Di Guglielmo, Giuseppe, Harris, Philip, Krupa, Jeffrey, Rankin, Dylan, Valentin, Manuel Blanco, Hester, Josiah, Luo, Yingyi, Mamish, John, Orgrenci-Memik, Seda, Aarrestad, Thea, Javed, Hamza, Loncar, Vladimir, Pierini, Maurizio, Pol, Adrian Alan, Summers, Sioni, Duarte, Javier, Hauck, Scott, Hsu, Shih-Chieh, Ngadiuba, Jennifer, Liu, Mia, Hoang, Duc, Kreinar, Edward, Wu, Zhenbin
Lenguaje:eng
Publicado: 2021
Materias:
physics.ins-det
Detectors and Experimental Techniques
cs.AR
Computing and Computers
cs.LG
Acceso en línea:http://cds.cern.ch/record/2754189

Internet

http://cds.cern.ch/record/2754189

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