Distributed Neural Network Model Parallelization for Memory Constraints
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Solution Overview
Problem
Deep learning models, despite their advancements in image, text, and audio recognition, struggle to match human-level perception and reasoning in analyzing large volumes of high-resolution images for fine-grained object recognition and scene understanding due to limitations in scalability and memory constraints.
Innovation Solution
A distributed neural network system that trains multiple local sub-networks on local processing devices, with each sub-network representing a subset of neurons from a global neural network, averaging trained local parameters to produce global parameters, enabling model and data parallelism and allowing training of large models even on devices with limited memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are enlarged in scale to match human-level perception and reasoning capacity, then recognition performance is improved, but memory constraints and scalability limitations worsen
Solution Approach 1:
The global neural network is segmented into multiple local sub-networks distributed across different processing devices. Each local sub-network contains a subset of neurons from each layer, allowing the large model to be divided into manageable chunks that fit within individual device memory constraints while collectively representing the full global model.
Solution Approach 2:
The patent introduces a new dimension of distribution by deploying neural network layers across multiple spatial devices rather than confining the entire model to a single device. This distributed architecture transforms the memory problem from a single-device constraint into a multi-device collaborative solution, enabling scaling beyond individual device memory limits.
2Measurement precision
If the size of neural network models is increased to analyze large volumes of high-resolution images, then fine-grained object recognition capability is improved, but device memory requirements worsen
Solution Approach 1:
The neural network model is segmented into local sub-networks distributed across multiple devices. Each device stores and processes only a portion of the total model parameters, reducing individual device memory requirements while maintaining the capability to perform fine-grained object recognition through coordinated computation across the distributed system.
Solution Approach 2:
Multiple copies of the distributed model parameters are maintained across different processing devices. Each device holds a copy of the subset of parameters it needs for local computation, enabling parallel processing of large image datasets without requiring any single device to store the entire model.
3Adaptability or versatility
If model parallelization is implemented across distributed devices, then scalability is improved, but system complexity worsens
Solution Approach 1:
The system is segmented into independent local processing devices, each handling a specific subset of neurons and parameters. This segmentation enables scalable deployment where devices can be added or removed based on computational needs, with each device operating semi-independently to reduce coordination overhead and system complexity.
Data Source
AI summary
Methods and systems for training a neural network include sampling multiple local sub-networks from a global neural network. The local sub-networks include a subset of neurons from each layer of the global neural network. The plurality of local sub-networks are trained at respective local processing devices to produce trained local parameters. The trained local parameters from each local sub-network are averaged to produce trained global parameters.