Hardware-Specific Neural Network Generation Through Model Compression

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Solution Overview

Problem

Generating multiple versions of neural networks for different platforms, computer systems, or hardware resources is time-consuming and resource-intensive due to the need for knowledge of diverse hardware and software configurations.

Innovation Solution

A system and method for training and generating compressed neural networks that are optimized for specific hardware resources by leveraging unique features and capabilities, using techniques such as model compression, dynamic tensor memory allocation, and sparsity, and employing software tools like TensorRT and OpenVINO to adapt neural networks for various platforms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple versions of neural networks are generated for different platforms and hardware resources, then adaptability and versatility are improved, but time consumption and computational resources increase significantly

Engineering Contradiction:

ImproveadaptabilityVSAvoidtime consumption

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent applies preliminary action by pre-training a generator neural network on a comprehensive dataset containing information about multiple platforms, computer systems, and hardware resources before it is needed. This pre-trained generator can then quickly generate platform-specific neural networks without requiring retraining for each new platform, thus resolving the contradiction between adaptability and time consumption

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent uses copying by having the generator neural network create multiple versions of neural networks tailored to different platforms from a single source model. The generator learns to copy and adapt the core functionality across different hardware configurations, enabling adaptability without generating each version from scratch, thereby reducing time and computational resources

Inventive Principle:

Principle #26Copying

2Adaptability or versatility

If multiple versions of neural networks are generated for different platforms and hardware resources, then adaptability and versatility are improved, but computational resources increase significantly

Engineering Contradiction:

ImproveversatilityVSAvoidcomputational resources

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The generator neural network learns to copy and adapt a single source neural network model across multiple platforms by training on a diverse dataset. This allows the system to generate versatile platform-specific versions without independently training separate models for each platform, significantly reducing computational resources and energy consumption while maintaining versatility

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent creates a universal generator neural network that can serve multiple functions by generating neural networks for different platforms, computer systems, and hardware resources. This single multi-functional generator replaces the need for multiple specialized training processes, improving versatility while reducing overall computational resource requirements

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Adaptability or versatility

If knowledge of different types of platforms, computer systems, and hardware resources is acquired, then adaptability is improved, but device complexity increases

Engineering Contradiction:

ImproveadaptabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary generator neural network that mediates between the source neural network and multiple target platforms. Instead of directly managing complex platform-specific configurations, the generator learns platform characteristics and automatically adapts models, reducing system complexity while maintaining adaptability across diverse hardware resources

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If neural networks are optimized for specific hardware resources using model compression and dynamic tensor memory allocation, then productivity is improved, but manufacturing precision may be affected

Engineering Contradiction:

ImproveefficiencyVSAvoidaccuracy

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The patent applies parameter changes by dynamically adjusting tensor memory allocation and applying model compression techniques tailored to specific hardware resources. The system modifies parameters such as precision, memory allocation, and computation depth based on target hardware capabilities, enabling efficient deployment while maintaining acceptable accuracy levels through adaptive parameter optimization

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250299020A1Neural network generation

Publication Date: 2025.09.25 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques to generate one or more neural networks. In at least one embodiment, a processor comprises one or more circuits to use one or more first neural networks to generate one or more second versions of one or more second neural networks based, at least in part, on one or more first versions of the one or more second neural networks and one or more hardware resources to be used to perform the one or more second versions of the one or more second neural networks.