Dynamic Neural Network Subnetwork Selection for Resource Adaptation
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Solution Overview
Problem
Conventional neural networks use a fixed amount of computational resources for processing inputs, which is inefficient as it does not account for varying resource availability, quality requirements, latency needs, or other factors, necessitating re-training for different environments.
Innovation Solution
A system that employs a task neural network with a controller neural network to dynamically adjust computational resources based on usage inputs, allowing for flexible resource allocation and joint training across different environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a conventional neural network uses a fixed amount of computational resources to process each network input, then the network structure is simple and easy to deploy, but it cannot adapt to varying resource availability, quality requirements, or latency needs across different environments
Solution Approach 1:
The neural network is segmented into multiple subnetworks with different computational complexities, where each subnetwork is optimized for specific resource constraints. The system divides the original network into alternative pathways that can be selected based on available resources, enabling adaptation without requiring a completely different network architecture for each environment.
Solution Approach 2:
The system dynamically selects which subnetwork to use based on real-time resource availability, quality requirements, and latency constraints. This dynamic adaptation allows the same network architecture to serve multiple environments by adjusting the active computational pathway rather than maintaining fixed resource usage.
2Manufacturing precision
If the computational complexity of the task neural network is increased to generate higher-quality outputs, then output quality improves, but processing time increases and latency increases
Solution Approach 1:
The system changes the parameter of computational complexity by selecting different subnetworks with varying levels of processing depth and width. When high output quality is required, a more complex subnetwork is activated; when low latency is prioritized, a simpler subnetwork is selected, thus dynamically adjusting the quality-time tradeoff based on requirements.
3Productivity
If the computational complexity of the task neural network is decreased to reduce processing time and latency, then speed improves, but output quality deteriorates
Solution Approach 1:
The system adjusts the computational complexity parameter by selecting appropriate subnetworks based on the priority between speed and quality. For tasks where speed is critical, simpler subnetworks provide faster processing; for tasks requiring accuracy, more complex subnetworks are deployed, thus resolving the speed-quality contradiction through parameter adjustment.
4Adaptability or versatility
If the task neural network is re-trained for different computational environments and quality requirements, then adaptability to specific environments improves, but computational resources and time required for training increase
Solution Approach 1:
The system achieves multi-environment adaptability through a single universal network architecture that contains multiple subnetworks capable of handling different resource constraints and quality requirements. This eliminates the need for separate training processes for each environment, as one trained architecture can dynamically adapt to various deployment scenarios through subnetwork selection.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for adjusting neural network resource usage. One of the methods includes receiving a network input for processing by a task neural network, the task neural network comprising a plurality of neural network layers; receiving a usage input specifying a respective weight for each of one or more usage factors, wherein each usage factor impacts how many computational resources are used by the task neural network during the processing of the network input; and processing the network input using the task neural network in accordance with the usage input to generate a network output for the network input, comprising: selecting, based at least on the usage input, a proper subset of the plurality of neural network layers to be active while processing the network input, and processing the network input using only the selected neural network layers.