Graphics Texture Compression and Decompression With Shared Neural Networks

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Solution Overview

Problem

Existing graphics processor texturing operations face inefficiencies in compression and decompression of graphics texture data, particularly due to the need for multiple hardware circuits and constrained formats, leading to increased bandwidth and storage requirements.

Innovation Solution

Implementing neural network-based compression and decompression schemes that allow a single neural network to handle multiple textures, reducing the need for multiple hardware circuits and optimizing bandwidth by reusing neural networks across texturing requests.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple hardware circuits are used for different texture compression algorithms, then compression and decompression functionality is improved, but device complexity increases

Engineering Contradiction:

Improvecompression and decompression functionalityVSAvoidhardware circuits

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies universality by implementing a single neural network architecture that can perform multiple texture compression and decompression operations. The neural network is designed with sufficient computational units and configurable parameters to handle different texture types and compression ratios, eliminating the need for separate dedicated hardware circuits for each compression algorithm while maintaining versatile functionality.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent replaces traditional mechanical hardware circuits with a neural network-based system. Instead of using dedicated hardware circuits for each compression algorithm, the invention uses a software-implemented neural network that can be configured to perform various compression and decompression tasks, thereby reducing hardware complexity while maintaining adaptability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If multiple dedicated hardware circuits are implemented for different compression algorithms, then compression performance is improved, but bandwidth requirements increase

Engineering Contradiction:

Improvecompression performanceVSAvoidbandwidth

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The neural network architecture is designed to handle multiple compression and decompression operations using a single unified structure. By configuring the neural network with appropriate parameters and computational units, the system achieves high compression performance without requiring multiple dedicated circuits that would increase bandwidth consumption. The single neural network reuses computational resources across different operations.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent merges multiple compression and decompression functions into a single neural network system. Instead of having separate hardware circuits for different algorithms that would require multiple data paths and increase bandwidth, the invention combines all these functions into one integrated neural network that processes different textures through shared computational resources, thereby reducing overall bandwidth requirements.

Inventive Principle:

Principle #5Merging (Combining)

3Adaptability or versatility

If multiple hardware circuits are used for texture decompression, then decompression capability is improved, but storage requirements increase

Engineering Contradiction:

Improvedecompression capabilityVSAvoidstorage requirements

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The neural network is designed as a universal decompression engine that can handle various texture formats and compression types using a single architecture. The network's computational units and parameters are configured to support multiple decompression scenarios, eliminating the need for storage of multiple dedicated hardware circuits and their associated configuration data, thereby reducing storage requirements while maintaining broad decompression capability.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250278864A1Graphics texture processing

Publication Date: 2025.09.04 ARM LTD

AI summary

Disclosed are methods of compressing/decompressing graphics texture data in particular in which a same neural network is used to compress/decompress multiple, different textures. This can therefore facilitate re-use of the same neural network during graphics processor operation when processing a sequence of texturing requests. Also disclosed are methods of processing graphics texture data in which a neural network is used to perform texturing filtering operations.