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| 001 | 1034067 | ||
| 005 | 20250203103442.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2024-06888 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-06888 |
| 100 | 1 | _ | |a Penke, Carolin |0 P:(DE-Juel1)192254 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a OpenGPT-X Forum 2024 |c Berlin |d 2024-11-05 - 2024-11-05 |w Germany |
| 245 | _ | _ | |a Mathematical Techniques to Reduce Memory Requirements in Deep Learning |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
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| 520 | _ | _ | |a We present a method to substantially lower memory requirements during the training of deep neural networks, based on the GaLore (Gradient Low-Rank Projection) training framework. A rapid decay of singular values in gradient matrices permits the use of low-rank bases to encapsulate the relevant subspaces, reducing the memory requirements for storing optimizer states between iterations. A novel, rank-adaptive, GPU-optimized version of the randomized range finder algorithm is employed to exploit this property and future research directions are discussed. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
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| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1034067/files/LowRankRepresentationsDL.pdf |y OpenAccess |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 914 | 1 | _ | |y 2024 |
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