Split Inference Model Selection Under Dynamic Network Conditions
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Solution Overview
Problem
Existing split inference systems in wireless communication networks do not adequately compensate for dynamic network conditions and changing requirements, leading to suboptimal performance.
Innovation Solution
Implement mechanisms for model selection in split inference across two devices, where the transmitter and receiver dynamically adjust encoder and decoder configurations based on distortion levels and task performance to optimize model selection and split points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If split inference is performed with fixed encoder and decoder configurations, then device complexity is reduced, but adaptability to dynamic network conditions deteriorates
Solution Approach 1:
The patent implements dynamic model selection where the transmitter and receiver can switch between different encoder-decoder model configurations based on current network conditions. The system transitions from static fixed configurations to dynamic adaptive configurations, allowing the split inference architecture to adjust its complexity and performance characteristics in response to changing bandwidth, latency requirements, and network reliability.
Solution Approach 2:
The system changes key parameters including the split point location in the neural network, encoder configuration types, and decoder configuration types based on network conditions. By varying these parameters dynamically, the system optimizes the balance between communication overhead and inference accuracy for different network scenarios without requiring complete reconfiguration of the entire system.
2Adaptability or versatility
If multiple model versions are supported for dynamic selection, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent segments the neural network into distinct encoder and decoder portions that can be independently configured and selected. Rather than managing multiple complete model versions, the system divides the inference task into separable components (encoder part and decoder part), allowing flexible combination of different configuration types without proportionally increasing overall complexity.
Solution Approach 2:
The system designs universal encoder and decoder configuration types that can handle multiple inference scenarios. The encoder configuration and decoder configuration are created as multi-functional components that can work together in various combinations to support different network conditions, reducing the need for highly specialized models for each scenario.
3Adaptability or versatility
If the split point is fixed in the task network, then ease of operation is improved, but adaptability to different network conditions deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the system monitors network conditions (bandwidth availability, latency, packet loss) and uses this information to dynamically adjust the split point location. The transmitter and receiver exchange information about current performance and network state, enabling automatic adaptation of the split configuration without manual intervention while maintaining operational simplicity through automated control loops.
Data Source
AI summary
Systems, methods, and devices for split inference for a task network split between two communication devices. Model parameters for each of the two task network portions may be determined based on a number of factors including the encoding/decoding configuration used for communicating the intermediated representations across the network, and/or the model performance based on network conditions or the encoding/decoding configuration. In some embodiments the transmitting device determines performance changes and/or model parameters and indicates to the receiving device. In some embodiments, the receiving device determines performance changes and/or model parameters and indicates to the transmitting device.