Hardware-Specific Neural Network Masking for Cross-GPU Efficiency
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Solution Overview
Problem
Neural network models exhibit suboptimal performance on different hardware resources due to varying constraints, necessitating improvements in performance across diverse platforms.
Innovation Solution
A processor modifies neural network models by generating variations through quantization and pruning, performing dry runs on different GPUs, and inserting masks based on performance metrics to create efficient models that meet or exceed performance thresholds on specific hardware platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a neural network model is trained on one hardware resource, then it achieves optimal performance on that hardware, but it exhibits suboptimal performance on different hardware resources
Solution Approach 1:
The patent segments the neural network model into multiple hardware-specific variations by dividing the model into layers and applying different quantization and pruning strategies to each layer based on hardware capabilities. This allows the model to be adapted to different hardware platforms without requiring complete retraining.
Solution Approach 2:
The patent changes model parameters such as data type precision (e.g., FP32, FP16, INT8), quantization levels, and pruning ratios to create hardware-specific model variations. By adjusting these parameters, the same base model can be optimized for different hardware resources while maintaining performance.
2Reliability
If neural network models are modified for specific hardware platforms, then performance on that hardware improves, but the complexity of model management increases
Solution Approach 1:
The patent creates a universal base neural network model that can serve multiple hardware platforms through a single codebase. The model incorporates hardware-agnostic operations with optional hardware-specific optimizations, allowing one codebase to support multiple platforms while maintaining hardware-specific performance benefits.
Solution Approach 2:
The patent implements dynamic model configuration where the model structure and parameters can be automatically adjusted based on the target hardware platform. This dynamic adaptation allows the system to select appropriate model variations without manual intervention, reducing management complexity.
3Adaptability or versatility
If multiple neural network model variations are generated for different hardware, then cross-platform performance improves, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing hardware capability information and model optimization guidelines before actual model generation. This preliminary preparation enables faster model adaptation to different hardware platforms without requiring time-consuming trial-and-error training processes.
Solution Approach 2:
The patent creates model variations by copying and modifying the base model structure rather than training entirely new models from scratch. This copying approach involves applying quantization and pruning operations to create hardware-specific variations, significantly reducing training time and computational resources required.
Data Source
AI summary
Apparatuses, systems, and techniques are to modify a neural network according to hardware type. In at least one embodiment, one or more masks are used with a neural network to be deployed on a specific GPU hardware platform.