Hierarchical Semantic Processing for Low-Latency Distributed Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for distributed semantic inference in mobile scenarios fail to provide semantic interpretation with regard to global goals, do not consider variable numbers of mobile devices, and neglect dynamic context changes, leading to inefficient communication and decision-making.
Innovation Solution
A device configured for distributed semantic processing and communication that maintains local context and goals, using an attention neural network and semantic extraction to process input data, allowing intermittent communication and efficient goal assignment across a parent-child hierarchy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If semantic processing is performed at distributed sensors, then communication costs and latency are reduced, but the complexity of coordinating semantic inference across multiple devices increases
Solution Approach 1:
The system segments the semantic processing task by assigning different roles to different devices: child devices perform local semantic inference independently, while parent devices coordinate and aggregate results. This segmentation allows each device to operate autonomously with reduced coordination overhead, resolving the contradiction between reduced latency and increased coordination complexity.
Solution Approach 2:
The patent introduces parent devices as intermediaries between child devices and the central controller. These intermediaries aggregate semantic information from multiple child devices and perform coordinated inference, reducing the direct coordination complexity between numerous sensors while maintaining low latency through hierarchical communication.
2Reliability
If continuous communication is maintained among all devices, then real-time coordination is improved, but communication costs and energy consumption increase
Solution Approach 1:
The system implements periodic communication where child devices transmit semantic information to parent devices at intervals rather than continuously. This periodic action maintains coordination reliability by updating states periodically while significantly reducing communication energy consumption compared to continuous communication.
Solution Approach 2:
Child devices autonomously determine when to communicate based on their local semantic processing needs and parent device requests. This self-service approach allows devices to maintain reliable coordination only when necessary, reducing unnecessary communication energy consumption while preserving coordination reliability.
3Adaptability or versatility
If the system adapts to variable numbers of mobile devices, then flexibility and adaptability are improved, but the complexity of maintaining context and goals increases
Solution Approach 1:
The system employs dynamic context management where parent devices maintain contextual information about child devices in a flexible data structure that automatically adapts to variable numbers of devices. This dynamic approach allows the system to handle device variability without increasing management complexity, as the context structure evolves with the network topology.
Solution Approach 2:
The parent device architecture provides universal functionality that works regardless of the number or type of child devices. The same parent device can manage any number of mobile devices with different sensor configurations, reducing the complexity of context management through a unified multi-functional approach.
4Speed
If semantic processing is performed locally at each device, then decision-making speed is improved, but the ability to perform semantic fusion across multiple sources is reduced
Solution Approach 1:
The system segments semantic processing into two levels: local semantic inference at child devices for fast decision-making, and global semantic fusion at parent devices for comprehensive multi-source analysis. This segmentation allows both fast local decisions and thorough semantic fusion to coexist without compromising either capability.
Solution Approach 2:
Parent devices serve as intermediaries that receive semantic information from multiple child devices, perform semantic fusion, and aggregate results. This intermediary approach preserves fast local decision-making at child devices while enabling comprehensive semantic fusion through the parent device layer.
Data Source
AI summary
A device for distributed semantic processing and communication is operable as a child device and/or a parent device in a parent-child hierarchy of devices. The device semantically processes input data based on a local context and a local goal. The input data originates from one or more sensors or one or more child devices of the device. The device maintains the local context based on the semantically processed input data and available side information. The device also participates in an assignment of respective local goals of the devices across the parent-child hierarchy based on the local context.