Neural Network Task Reassignment for Multitask Learning Trade-Offs
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Solution Overview
Problem
Existing neural network architectures face performance trade-offs when multiple tasks are assigned to a single network, particularly in scenarios requiring high-speed processing and low power consumption, leading to inefficient learning and suboptimal performance.
Innovation Solution
A learning apparatus that assigns different tasks to multiple neural networks and dynamically adjusts task assignments based on learning results, using history information to optimize task distribution and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If multiple tasks are assigned to one neural network to improve resource utilization, then device complexity is reduced, but task performance deteriorates due to trade-off relationships between tasks
Solution Approach 1:
The patent applies dynamics by making the task assignment configuration changeable over time. The system dynamically adjusts which tasks are assigned to which neural networks based on learning progress, allowing the configuration to evolve from a static initial state to an optimized state that resolves performance trade-offs while maintaining resource efficiency
Solution Approach 2:
The patent changes the parameter of task assignment configuration from fixed to variable. By modifying which tasks are assigned to which networks based on learning results and historical data, the system optimizes the balance between resource utilization and task performance without increasing device complexity
2Productivity
If a small network size is used to achieve high-speed processing and low power consumption, then productivity is improved, but task performance deteriorates due to trade-off relationships
Solution Approach 1:
The system dynamically adjusts task assignments in small networks based on learning progress, allowing small networks to achieve both high processing speed and acceptable task performance by optimizing which tasks they handle at different stages of learning
Solution Approach 2:
The patent segments tasks into different groups that are assigned to different neural networks based on their characteristics and performance requirements. This allows small networks to specialize in specific tasks where they can achieve high speed without sacrificing overall system performance
3Device complexity
If inappropriate task combinations are assigned to neural networks, then device complexity is reduced, but learning time increases and target performance cannot be achieved
Solution Approach 1:
The system uses feedback from learning results and historical data to continuously optimize task assignments. By monitoring performance metrics and adjusting assignments accordingly, the system avoids inappropriate combinations that would waste learning time while maintaining simple device complexity
Solution Approach 2:
The patent performs preliminary analysis of task characteristics and learning patterns to pre-determine optimal assignment configurations. This preliminary action prevents inappropriate task combinations before they occur, reducing unnecessary learning time while keeping the assignment configuration manageable
Data Source
AI summary
A learning apparatus performs a first assignment in which M tasks that are different from each other are assigned to N neural networks (where N<M) and perform learning processing that is related to the M tasks in parallel; and determines, based on learning results of the respective M tasks, whether to assign, in subsequent learning processing, the respective M tasks to the same neural networks as in the first assignment or to neural networks different from those of the first assignment.