Continuous 16-bit Training: Accelerating 32-bit Pre-Trained Neural Networks

• URL: http://arxiv.org/abs/2311.18587v2
• Date: Fri, 1 Dec 2023 02:51:32 GMT
• Title: Continuous 16-bit Training: Accelerating 32-bit Pre-Trained Neural Networks
• Authors: Juyoung Yun
• Abstract summary: This study introduces a novel approach where we continue the training of pre-existing 32-bit models using 16-bit precision. By adopting 16-bit precision for ongoing training, we are able to substantially decrease memory requirements and computational burden. Our experiments show that this method maintains the high standards of accuracy set by the original 32-bit training while providing a much-needed boost in training speed.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In the field of deep learning, the prevalence of models initially trained with 32-bit precision is a testament to its robustness and accuracy. However, the continuous evolution of these models often demands further training, which can be resource-intensive. This study introduces a novel approach where we continue the training of these pre-existing 32-bit models using 16-bit precision. This technique not only caters to the need for efficiency in computational resources but also significantly improves the speed of additional training phases. By adopting 16-bit precision for ongoing training, we are able to substantially decrease memory requirements and computational burden, thereby accelerating the training process in a resource-limited setting. Our experiments show that this method maintains the high standards of accuracy set by the original 32-bit training while providing a much-needed boost in training speed. This approach is especially pertinent in today's context, where most models are initially trained in 32-bit and require periodic updates and refinements. The findings from our research suggest that this strategy of 16-bit continuation training can be a key solution for sustainable and efficient deep learning, offering a practical way to enhance pre-trained models rapidly and in a resource-conscious manner.

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