Multi-Chip Sensor Hub Context Mapper Routing
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Solution Overview
Problem
The increasing complexity of sensor data processing in autonomous vehicles, due to the need for redundant sensors and processors, leads to high volumes of real-time data that must be efficiently routed and processed.
Innovation Solution
A multi-chip hub system that efficiently connects multiple sensing devices and processing devices, using a context mapper to route data to appropriate processing components and storage locations, allowing for high processing power and fail-safe operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If redundant sensors and processors are added to improve safety and reliability, then system reliability is improved, but device complexity and data processing load increase
Solution Approach 1:
A hub component is introduced as an intermediary between sensors and processors. The hub receives data from multiple sensors, manages data routing based on context identifiers, and distributes data to appropriate processors. This mediator simplifies the overall system architecture by centralizing data management functions and reducing the complexity of direct connections between sensors and multiple processors.
Solution Approach 2:
The system segments data processing by assigning unique context identifiers to different data streams from sensors. The hub divides and routes data based on these identifiers to specific processors, enabling modular and organized processing. This segmentation allows redundant processors to handle specific data streams independently while maintaining overall system reliability.
2Measurement precision
If high volume of real-time data is processed from multiple sensors, then measurement precision is improved, but loss of time in data routing and processing increases
Solution Approach 1:
The hub pre-configures routing paths and data management structures based on context identifiers before data processing begins. By establishing these pathways in advance, the system can rapidly route high-volume real-time data from multiple sensors to appropriate processors without significant delay, maintaining both precision and speed.
3Reliability
If multiple copies of sensor data are delivered to redundant processors, then reliability is improved, but productivity of data processing decreases
Solution Approach 1:
The hub dynamically manages data distribution by adjusting routing decisions based on real-time system conditions and processor availability. This dynamic approach allows the system to maintain reliability through redundant processing while optimizing throughput by directing data to the most appropriate processors at any given moment, avoiding unnecessary data copying when not needed.
Data Source
AI summary
Various systems and methods are provided. One such system includes first and second inputs of first and second types, respectively; a data controller, including a context mapper coupled to the first and second inputs. The data controller includes a context mapper that provides a processing identifier and a storage identifier to each item of data received from the first and second inputs; and a set of processing components, each coupled to the context mapper, and each associated with a respective processing identifier for processing each item of data having the corresponding processing identifier. The system further includes a memory coupled to the context mapper, the memory having multiple storage locations each associated with a respective storage identifier for storing each item of data having the corresponding storage identifier; and first and second outputs of the first and second types, respectively, coupled to the data controller.