Distributed Neural Data Compression With Adaptive Edge-Cloud Encoding
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Solution Overview
Problem
Existing lossy compression methods lack adaptability to dynamic data characteristics and application-specific requirements, failing to effectively capture temporal dependencies and optimize compression efficiency across diverse applications.
Innovation Solution
A distributed data compression system that leverages edge and central computing, incorporating a lightweight compression subsystem at the edge and advanced temporal modeling at the central system, dynamically optimizing compression parameters and task allocation to balance efficiency and reconstruction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression techniques are used to achieve higher compression ratios, then data transmission efficiency is improved, but reconstruction quality deteriorates
Solution Approach 1:
The patent implements dynamic adjustment of compression parameters based on data characteristics and application requirements. The system adapts compression strength, temporal modeling depth, and quantization levels in real-time to optimize the balance between compression ratio and reconstruction quality for different data types and network conditions.
Solution Approach 2:
The system changes multiple compression parameters including compression strength, temporal window size, and quantization bits based on data statistics and application needs. This allows the system to achieve high compression ratios for tolerant applications while maintaining high reconstruction quality for sensitive applications like medical imaging.
2Device complexity
If fixed parameter compression methods are used, then system complexity is reduced, but adaptability to dynamic data characteristics deteriorates
Solution Approach 1:
The system performs preliminary analysis of data characteristics (temporal dependencies, data type, application requirements) before compression to determine optimal parameters. This preliminary characterization enables the system to adapt to different data types without requiring complex real-time adjustments during compression.
Solution Approach 2:
The compression system automatically analyzes its own input data characteristics and self-adjusts compression parameters without external intervention. The system services itself by detecting data patterns and selecting appropriate compression strategies, reducing the need for manual configuration while maintaining high adaptability.
3Power
If centralized processing is used, then computational capability is improved, but response time and resource utilization in distributed environments deteriorates
Solution Approach 1:
The patent segments the compression system into edge computing components (local preprocessing, initial compression) and centralized cloud components (advanced temporal modeling, final optimization). This segmentation allows resource-constrained edge devices to perform immediate compression while the cloud handles computationally intensive tasks, reducing overall response time and improving resource utilization.
Data Source
AI summary
A system for distributed data compression leverages edge and central computing devices to optimize efficiency and quality. The system includes a lightweight compression subsystem at the edge device that preprocesses and partially compresses input data before transmitting it to a central computing device. The central compression subsystem further processes the data using adjustable compression parameters and a temporal modeling component, reconstructing the data with high fidelity. The system dynamically optimizes preprocessing, encoding, and reconstruction operations based on resource availability, network conditions, and application-specific criteria. This distributed approach improves bandwidth efficiency and adaptability, making it suitable for applications requiring low-latency or resource-constrained environments.