Neural Network Model Shared Dedicated Weights Task Switching
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Solution Overview
Problem
Neural network models face performance degradation when processing multiple tasks due to inefficient weight value utilization, leading to reduced processing efficiency and increased data read times during task switching.
Innovation Solution
The neural network model incorporates M network layers with a shared weight value and N groups of dedicated weight values, where each group corresponds to a specific task, allowing the ith network layer to perform calculations using only the shared weight value and the dedicated weight values for the current task, reducing the need for repeated weight value retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a neural network model uses separate dedicated weight values for each task, then task-specific performance is improved, but the quantity of weight values and storage requirements increase
Solution Approach 1:
The weight values are segmented into two distinct groups: shared weight values that are common across all tasks, and dedicated weight values that are specific to each task. This segmentation allows the model to maintain task-specific performance while reducing overall storage requirements by eliminating redundant weight values.
Solution Approach 2:
The shared weight values serve multiple tasks simultaneously, making them universal across different task types. This multi-functionality reduces the total quantity of weight values needed while maintaining effective performance across all N tasks through the combination of shared and dedicated weights.
2Measurement precision
If the neural network model retrieves dedicated weight values for each task separately, then task processing accuracy is improved, but data read time increases during task switching
Solution Approach 1:
The model performs preliminary organization of weight values by separating them into shared and dedicated groups before task execution. During task switching, the system can quickly access the pre-organized dedicated weight values for the current task while the shared weight values remain available, significantly reducing data read time while maintaining processing accuracy.
3Quantity of substance
If the neural network model uses a shared weight value for all tasks, then storage requirements are reduced, but performance on individual tasks deteriorates
Solution Approach 1:
Different parts of the weight value system have different qualities and functions: shared weight values provide general feature extraction capability common to all tasks, while dedicated weight values provide task-specific refinement. This local differentiation of quality ensures both efficient storage utilization and high individual task performance.
Data Source
AI summary
A neural network model of M network layers, a data processing method, and a processing apparatus configured to execute N tasks, where an ith network layer has a shared weight value to execute each of the N tasks and N groups of dedicated weight values, where each of the N groups of dedicated weight values executes one of the N tasks, all the groups of dedicated weight values are in a one-to-one correspondence with the N tasks, M is a positive integer and 1≤i≤M, when executing a first task, the ith network layer is configured to obtain input data, obtain output data based on a tth group of dedicated weight values, the shared weight value, and the input data, when 1≤i≤M, transmit the output data to an (i+1)th network, where the tth group of dedicated weight values corresponds to the first task, and when i=M, output the output data.