What is it about?
This paper presents a method to enhance the training of large and complex neural networks. It focuses on efficient memory management by using learning algorithms to optimize how memory is allocated and utilized during training. This approach allows for dynamic adjustment of memory resources, enabling the handling of larger models and datasets without running into memory limitations. Ultimately, the paper aims to improve the training efficiency and scalability of neural networks, making it feasible to work with more advanced AI models.
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This page is a summary of: Enabling Large Dynamic Neural Network Training with Learning-based Memory Management, March 2024, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/hpca57654.2024.00066.
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