Multi-GPU Neural Network Training Workload Segmentation
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Solution Overview
Problem
Current neural network training techniques are time-consuming and resource-intensive due to the large amounts of data involved and the computational demands of preprocessing, storing, and transferring data, especially when using multi-processor configurations for real-time deep learning applications.
Innovation Solution
Implementing a multi-GPU configuration within a computing environment where one GPU renders images and another GPU performs preprocessing, training, and inference using a deep learning framework, allowing for real-time feedback and optimization of neural network weights, thereby speeding up the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional single-processor neural network training is used, then implementation simplicity is maintained, but training time and resource consumption increase significantly
Solution Approach 1:
The patent divides the neural network training workload into separate segments handled by different processors. Specifically, one processor (first computing device) is dedicated to data preprocessing and augmentation, while another processor (second computing device) handles the actual neural network training. This segmentation allows each processor to specialize in specific tasks, improving overall training efficiency and reducing bottlenecks associated with single-processor sequential processing.
Solution Approach 2:
The patent combines multiple computing devices to work together on the neural network training task. By merging the capabilities of multiple processors through inter-process communication and coordinated task execution, the system achieves parallel processing benefits that significantly reduce training time while distributing the computational burden across multiple devices.
2Measurement precision
If large amounts of data are processed for neural network training, then model accuracy improves, but data transfer and preprocessing time increase
Solution Approach 1:
The patent applies preliminary action by performing data preprocessing, augmentation, and preparation tasks before the actual neural network training begins. The first computing device preprocesses the training data in advance, creating augmented and optimized datasets that are then ready for immediate use by the second computing device during training. This eliminates the need for real-time data processing during training iterations, significantly reducing time loss while maintaining access to large, high-quality datasets for improved model accuracy.
Data Source
AI summary
Apparatuses, systems, and techniques for neural network training and inference using multiple processor resources. In at least one embodiment, one or more neural networks are used to generate one or more second versions of one or more images based, at least in part, on a first version of the one or more images and a three-dimensional representation of the one or more first versions of the one or more images.