Super System on Chip with Photonic Neural Learning Processor
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Solution Overview
Problem
Current systems lack an integrated solution for providing real-time, ambient, and pervasive user experiences across connected objects, bioobjects, and intelligent vehicles, failing to effectively leverage advanced algorithms and IoT technologies for seamless interaction and data management.
Innovation Solution
The development of a system and method that integrates intelligent algorithms, cloud expert systems, and quantum internet capabilities with wearable devices and vehicles, enabling real-time interactions through object nodes, bioobject nodes, and intelligent appliances, utilizing machine learning, deep learning, and self-learning algorithms for data processing and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If systems integrate multiple intelligent algorithms and IoT technologies for real-time data processing, then user experience and decision-making capabilities are enhanced, but device complexity and computational resource requirements increase
Solution Approach 1:
The system divides complex data processing tasks into distributed computations across multiple object nodes, bioobject nodes, and cloud infrastructure. Each node processes specific functions independently, reducing individual device complexity while maintaining overall system productivity through parallel processing.
Solution Approach 2:
The patent introduces intermediary layers including edge computing devices and gateway protocols that mediate between heterogeneous IoT devices and cloud systems. These intermediaries standardize communication protocols and data formats, reducing integration complexity while enabling efficient data flow across the system.
2Adaptability or versatility
If ambient and pervasive sensing is deployed across multiple devices, then user experience coverage is improved, but energy consumption and data management overhead increase
Solution Approach 1:
The system implements periodic sampling and event-triggered data transmission rather than continuous monitoring. Sensors activate only when specific conditions are met or at optimized intervals, reducing energy consumption while maintaining comprehensive coverage through strategic placement of multiple nodes.
Solution Approach 2:
The patent combines multiple sensing functions into integrated sensor nodes that simultaneously perform environmental monitoring, user tracking, and health detection. This consolidation reduces the total number of devices required, lowering overall energy consumption while maintaining pervasive coverage.
3Loss of time
If real-time processing is implemented across distributed devices, then response time is reduced, but synchronization complexity and communication overhead increase
Solution Approach 1:
The system pre-synchronizes clocks and establishes communication protocols before distributed processing begins. Time stamps and coordination metadata are embedded in data packets in advance, enabling nodes to process data independently while maintaining temporal coherence without complex real-time synchronization overhead.
Data Source
AI summary
A Super System on Chip (SSoC) coupled with a photonic neural learning processor (PNLP), one or more quantum bits (qubits) and a machine learning algorithm for ultrafast data processing, image processing/recognition, deep learning/meta-learning and self-learning is disclosed. The Super System on Chip (SSoC) is interconnected/coupled electrically and/or optically in two-dimension (2-D) or in three-dimension (3-D).