Edge Neural Network Updates Using Difference Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions for deploying and updating neural networks on edge devices with limited bandwidth and compute resources face challenges, particularly in scenarios with restricted, intermittent, and non-reliable connectivity, leading to inefficient and inaccurate inference operations.
Innovation Solution
Methods for updating neural networks on edge devices include generating a neural network difference model by comparing the updated and trained networks, using techniques like layer freezing and minimum delta techniques, and sending only metadata or small data parts to minimize bandwidth usage, while enabling edge-based inference and transfer learning from similar devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire trained neural network is sent to the edge device for updates, then the inference accuracy is improved, but the bandwidth consumption increases significantly
Solution Approach 1:
The patent segments the neural network update process into two parts: (1) sending only the difference model (patches) from centralized site to edge device, and (2) applying these patches locally to update the neural network. This segmentation allows incremental updates without transmitting the entire network, significantly reducing bandwidth consumption while maintaining inference accuracy.
Solution Approach 2:
The patent extracts and transmits only the essential update information (difference models representing weight changes) rather than the complete neural network. By taking out only the necessary patches that contain the update deltas, the system minimizes data transmission while ensuring accuracy improvements are delivered to the edge device.
2Measurement precision
If frequent updates are performed to improve inference accuracy, then the model performance is enhanced, but the power consumption increases due to repeated data transmission
Solution Approach 1:
The update mechanism is segmented into incremental patches that can be applied frequently without requiring full network retransmission. Each patch represents a small, targeted update that can be transmitted and applied quickly, enabling frequent accuracy improvements with minimal power expenditure per update cycle.
Solution Approach 2:
The system implements periodic incremental updates using difference models rather than continuous full-network transmissions. Updates are performed at optimized intervals where only the necessary weight changes are transmitted, allowing the edge device to maintain improved accuracy while consuming less power compared to frequent full updates.
3Adaptability or versatility
If the neural network is updated in real-time at the edge device, then the adaptability is improved, but the computational complexity increases
Solution Approach 1:
The patent extracts the computationally intensive training operations from the edge device and concentrates them at the centralized site. Only the simplified difference model (weight deltas) is transmitted to the edge device for application. This extraction allows real-time adaptability at the edge while keeping the computational complexity manageable through the use of lightweight patch application operations.
4Loss of information
If more data is transmitted to enable better model updates, then the learning quality is improved, but the bandwidth requirements increase
Solution Approach 1:
The system extracts and transmits only the essential learning information in the form of difference models that represent weight changes. By taking out only the critical update deltas rather than transmitting complete datasets or full network states, the patent maintains learning quality while significantly reducing bandwidth requirements for model updates.
Solution Approach 2:
The update data is segmented into targeted patches that contain only the necessary weight changes for specific network layers or components. This segmentation allows the system to transmit minimal data while preserving learning quality, as each patch contains precisely the information needed for accurate model updates without redundant data transmission.
Data Source
AI summary
Methods, devices and system for updating a neural network on an edge device that has low-bandwidth uplink capability include a centralized site/device that is configured to train and send the neural network to the edge device. In response, the centralized site/device may receive neural network information from the edge device that includes all or portions of a dataset, output activations, and/or overall inference result that is collected or generated in the edge device. The centralized site/device may use the received neural network information to update all or a part of the trained neural network, generate updated neural network information based on the updated neural network, and send the updated neural network information to the edge device.