Published October 15, 2023
| Version v1
Poster
Open
Open-source codesign of machine learning algorithms on FPGAs for scientific discovery
Authors/Creators
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1.
University of California San Diego
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2.
Massachusetts Institute of Technology
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3.
ETH Zurich
Description
Poster describing hls4ml and Anomaly eXtraction Online Level-1 Trigger Lightweight algorithm for the 2023 NSF HDR Ecosystem Conference.
Files
hdr_hls4ml_axol1tl_poster.pdf
Files
(28.1 MB)
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Additional details
Related works
- Cites
- Software: 10.5281/zenodo.7933047 (DOI)
- Journal article: 10.3389/fdata.2022.787421 (DOI)
- Journal article: 10.1088/1748-0221/15/10/P10017 (DOI)
- Journal article: 10.1088/1748-0221/13/07/P07027 (DOI)
- Journal article: 10.1038/s42256-022-00441-3 (DOI)
References
- Front. Big Data 5, 787421(2022)
- JINST 15, P10017 (2020)
- JINST 13, P07027 (2018)
- https://doi.org/10.5281/zenodo.7933047
- Nat. Mach. Intell. 4, 154 (2022)