Graph Diffusion Pruning for Neural Network Compression Signaling
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Solution Overview
Problem
Existing methods for compressing and transporting neural networks are inefficient and lack standardized formats for exchange, particularly for compressed neural networks.
Innovation Solution
The use of graph diffusion techniques for structured pruning of neural networks, combined with signaling mechanisms for decoding pruned neural networks, addresses the inefficiencies in existing methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If graph diffusion techniques are used for structured pruning, then compression efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by performing graph diffusion analysis before the actual pruning operation. The importance scores of neurons are calculated in advance using graph diffusion processes, allowing the system to identify which neurons to remove before executing the pruning. This preliminary assessment enables more efficient compression while managing computational complexity through staged processing.
Solution Approach 2:
The patent segments the neural network into discrete layers and processes each layer independently through graph diffusion. By dividing the network into manageable segments (layers) and applying pruning operations layer by layer, the system improves compression efficiency while controlling computational complexity through modular processing rather than attempting to prune the entire network simultaneously.
2Quantity of substance
If neurons are removed to prune the neural network, then model size is reduced, but processing speed may be affected
Solution Approach 1:
The patent changes parameters by systematically removing neurons based on importance scores derived from graph diffusion analysis. By identifying and removing less important neurons while preserving critical ones, the model size is reduced while maintaining processing speed through intelligent parameter selection rather than random or uniform pruning.
Solution Approach 2:
The patent replaces mechanical uniform pruning with an intelligent selection mechanism based on graph diffusion importance scores. Instead of removing neurons through simple mechanical means (random or uniform removal), the system uses computational analysis to identify which neurons to remove, substituting brute-force methods with informed decision-making that preserves processing speed.
3Adaptability or versatility
If standardized formats are implemented for neural network exchange, then interoperability is improved, but implementation complexity increases
Solution Approach 1:
The patent implements universality by creating standardized formats for representing pruned neural networks that can be exchanged between different systems and platforms. The standardized representation allows the same pruning methodology to be applied across diverse neural network architectures and deployment environments, improving interoperability while managing implementation complexity through consistent formatting rules.
Data Source
AI summary
An apparatus includes at least one processor; and at least one non-transitory memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to: estimate an importance of parameters of a neural network based on a graph diffusion process over at least one layer of the neural network; determine the parameters of the neural network that are suitable for pruning or sparsification; remove neurons of the neural network to prune or sparsify the neural network; and provide at least one syntax element for signaling the pruned or sparsified neural network over a communication channel, wherein the at least one syntax element comprises at least one neural network representation syntax element.