Latent Weight Quantization for Integerized Training of Deep Neural Networks
Existing methods for integerized training speed up deep learning by using low-bitwidth integerized weights, activations, gradients, and optimizer buffers. However, they overlook the issue of full-precision latent weights, which consume excessive memory to accumulate gradient-based updates for optimi...
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Détails bibliographiques
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 47(2025), 4 vom: 15. Apr., Seite 2816-2832
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| Auteur principal: |
Fei, Wen
(Auteur) |
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| Autres auteurs: |
Dai, Wenrui,
Zhang, Liang,
Zhang, Luoming,
Li, Chenglin,
Zou, Junni,
Xiong, Hongkai |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2025
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence
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| Sujets: | Journal Article |
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