Neural Network Architecture Adjustment for Cross-Platform Inference
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Solution Overview
Problem
Neural networks require significant energy, time, and compute resources for training and are often not deployable across different hardware-software platforms without multiple training runs due to distinct hardware and software features.
Innovation Solution
A dynamic refined model is generated by adjusting neural network architectures using dynamic parameters specific to each target platform, optimizing the network for inference on various hardware and software configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained separately for each hardware-software platform, then deployment accuracy is improved, but training time and compute resources increase significantly
Solution Approach 1:
The patent applies preliminary action by training a single universal neural network model in advance that can adapt to multiple hardware-software platforms. Instead of performing separate training runs for each platform, the model is pre-trained with platform-agnostic features and then fine-tuned or adjusted for specific platforms during deployment, significantly reducing the time and computational resources required while maintaining deployment accuracy.
Solution Approach 2:
The patent implements universality by creating a single neural network model that serves multiple hardware-software platforms. The model is designed with platform-independent architecture and training approaches, allowing it to function effectively across different target platforms without requiring separate training processes for each platform, thus improving efficiency while maintaining accuracy.
2Adaptability or versatility
If multiple training runs are performed for different hardware platforms, then platform-specific optimization is improved, but energy consumption increases
Solution Approach 1:
The patent applies preliminary action by performing platform-specific optimizations in advance during the model training phase rather than during deployment. A universal model is pre-trained that incorporates adaptations for multiple platforms, allowing the system to achieve platform-specific optimization without incurring the energy costs of multiple separate training runs at deployment time.
Solution Approach 2:
The patent utilizes parameter changes by adjusting model parameters and architecture configurations based on target platform characteristics. Instead of retraining the entire model for each platform, the system modifies specific parameters such as layer configurations, activation functions, or weight initializations to optimize performance for different hardware-software environments, thereby reducing energy consumption while maintaining adaptability.
3Measurement precision
If multiple training runs are conducted for each hardware platform, then deployment accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements universality by developing a single universal neural network model that can be deployed across multiple hardware-software platforms. This universal model eliminates the need for maintaining separate training processes for each platform, simplifying the deployment architecture while preserving platform-specific optimization capabilities through parameter adjustments rather than structural changes.
Solution Approach 2:
The patent applies preliminary action by consolidating platform-specific adaptations into the initial model training phase. The universal model is pre-configured with platform-agnostic features and can be subsequently adapted to specific platforms through lightweight adjustments, thereby reducing the complexity of managing multiple training processes while maintaining deployment accuracy across different platforms.
Data Source
AI summary
Apparatuses, systems, and techniques to generate neural networks optimized for different hardware. In at least one embodiment, a processor using circuits is to adjust a neural network architecture based on computing resources that use said neural network in inference.