NIST · ИТ · 264c64ffddeb21dc1e20
Low-Rank Gradient Descent for Memory-Efficient Training of Deep In-Memory Arrays
The movement of large quantities of data during the training of a Deep Neural Network presents immense challenges for machine learning workloads. To minimize this overhead, espe- cially on the movement and calculation of gradient information, we introduce
NIST · ИТОфициальный источник · 2023-05-18
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"title_en": "Low-Rank Gradient Descent for Memory-Efficient Training of Deep In-Memory Arrays",
"designation": "",
"series": "",
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"date": "2023-05-18",
"date_original": "May 18, 2023",
"url": "https://www.nist.gov/publications/low-rank-gradient-descent-memory-efficient-training-deep-memory-arrays",
"source": "NIST Publications",
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"authors_en": "Siyuan Huang , Brian Hoskins , Matthew Daniels , Mark Stiles , Gina C. Adam",
"abstract_excerpt_en": "The movement of large quantities of data during the training of a Deep Neural Network presents immense challenges for machine learning workloads. To minimize this overhead, espe- cially on the movement and calculation of gradient information, we introduce",
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"title_en": "Low-Rank Gradient Descent for Memory-Efficient Training of Deep In-Memory Arrays",
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"date": "2023-05-18",
"date_original": "May 18, 2023",
"url": "https://www.nist.gov/publications/low-rank-gradient-descent-memory-efficient-training-deep-memory-arrays",
"source": "NIST Publications",
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"authors_en": "Siyuan Huang , Brian Hoskins , Matthew Daniels , Mark Stiles , Gina C. Adam",
"abstract_excerpt_en": "The movement of large quantities of data during the training of a Deep Neural Network presents immense challenges for machine learning workloads. To minimize this overhead, espe- cially on the movement and calculation of gradient information, we introduce"
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