Neural Network Hardware Accelerator for Efficient Transfer Learning
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Solution Overview
Problem
Neural networks require extensive training time and resources, especially for power-constrained devices, and existing methods struggle to efficiently adapt to new tasks without significant retraining.
Innovation Solution
Implementing a hardware accelerator with fixed, domain-specific feature generators and a parallel programmable module for fine-tuning, allowing for efficient transfer learning by hardcoding certain layers and updating others, reducing power consumption and training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural networks are trained extensively to achieve high accuracy, then model performance is improved, but training time and energy consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training neural network models on large datasets before deployment. The system performs training in advance during manufacturing or initialization, so that when the device is deployed in power-constrained environments, the model is already trained and can perform inference without requiring extensive additional training time.
Solution Approach 2:
The patent segments the training process into distinct phases: initial comprehensive training on large datasets, followed by selective fine-tuning on specific domain data. This segmentation allows the model to achieve high accuracy through preliminary training while enabling efficient adaptation to new tasks through targeted updates of only certain weight parameters rather than complete retraining.
2Reliability
If neural networks are trained extensively to achieve high accuracy, then model performance is improved, but energy consumption increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-training neural network models on large datasets before deployment. The system performs training in advance during manufacturing or initialization, so that when the device is deployed in power-constrained environments, the model is already trained and can perform inference without requiring extensive additional training time.
Solution Approach 2:
The patent applies parameter changes by selectively updating only specific weight parameters in the neural network during fine-tuning, rather than retraining all parameters. This allows the model to adapt to new tasks or domains by modifying only the necessary parameters while keeping the pre-trained parameters fixed, significantly reducing the computational energy required for adaptation.
3Adaptability or versatility
If neural networks are retrained significantly to adapt to new tasks, then task specificity is improved, but training resources and time increase
Solution Approach 1:
The patent applies parameter changes by selectively updating only specific weight parameters in the neural network during fine-tuning, rather than retraining all parameters. This allows the model to adapt to new tasks or domains by modifying only the necessary parameters while keeping the pre-trained parameters fixed, significantly reducing the computational energy required for adaptation.
Solution Approach 2:
The patent segments the training process into distinct phases: initial comprehensive training on large datasets, followed by selective fine-tuning on specific domain data. This segmentation allows the model to achieve high accuracy through preliminary training while enabling efficient adaptation to new tasks through targeted updates of only certain weight parameters rather than complete retraining.
Data Source
AI summary
A system, an apparatus and methods for utilizing software and hardware portions of a neural network to fix, or hardwire, certain portions, while modifying other portions are provided. A first set of weights for layers of the first neural network are established, and selected weights are modified to generate a second set of weights, based on a second dataset. The second set of weights is then used to train a second neural network.