Partitioned Neural Network Training via Synthetic Intermediate Labels

• URL: http://arxiv.org/abs/2403.11204v1
• Date: Sun, 17 Mar 2024 13:06:29 GMT
• Title: Partitioned Neural Network Training via Synthetic Intermediate Labels
• Authors: Cevat Volkan Karadağ, Nezih Topaloğlu,
• Abstract summary: GPU memory constraints have become a notable bottleneck in training such sizable models. This study advocates partitioning the model across GPU and generating synthetic intermediate labels to train individual segments. This approach results in a more efficient training process that minimizes data communication while maintaining model accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of extensive neural network architectures, particularly deep learning models, presents a challenge in terms of resource-intensive training. GPU memory constraints have become a notable bottleneck in training such sizable models. Existing strategies, including data parallelism, model parallelism, pipeline parallelism, and fully sharded data parallelism, offer partial solutions. Model parallelism, in particular, enables the distribution of the entire model across multiple GPUs, yet the ensuing data communication between these partitions slows down training. Additionally, the substantial memory overhead required to store auxiliary parameters on each GPU compounds computational demands. Instead of using the entire model for training, this study advocates partitioning the model across GPUs and generating synthetic intermediate labels to train individual segments. These labels, produced through a random process, mitigate memory overhead and computational load. This approach results in a more efficient training process that minimizes data communication while maintaining model accuracy. To validate this method, a 6-layer fully connected neural network is partitioned into two parts and its performance is assessed on the extended MNIST dataset. Experimental results indicate that the proposed approach achieves similar testing accuracies to conventional training methods, while significantly reducing memory and computational requirements. This work contributes to mitigating the resource-intensive nature of training large neural networks, paving the way for more efficient deep learning model development.

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