Adaptive Multi-Node AI Topology for Load Balancing
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Solution Overview
Problem
Current cloud-based AI services face limitations such as high costs, unpredictable performance due to overload or denial-of-service attacks, and unavailability when users are offline, with AI models being over-engineered for general use but under-engineered for specific user needs, leading to inefficient resource usage and user dissatisfaction.
Innovation Solution
The development of adaptive multi-node AI topologies that combine reconfigurable neural network-based circuit units and software AI models, allowing for local and remote operation, with configuration data sharing to enable efficient resource allocation, load balancing, and personalized AI performance without the need for constant cloud interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud-based AI services are used to provide high processing power and storage, then AI model performance and capability are improved, but user costs and service fees increase
Solution Approach 1:
The patent segments the AI system into multiple distributed nodes instead of relying on a centralized cloud service. Each node independently provides AI processing capabilities, dividing the system into smaller, more efficient units that reduce per-user costs while maintaining overall processing power through distributed computation.
Solution Approach 2:
The patent creates a multi-functional AI node that can serve multiple purposes: local processing, cloud synchronization, collaborative computation, and offline operation. This universal node design eliminates the need for separate cloud and local systems, reducing redundant costs while providing comprehensive AI capabilities.
2Adaptability or versatility
If cloud-based AI services operate as independent models for general use, then service versatility is improved, but performance for specific user needs deteriorates
Solution Approach 1:
The patent implements local quality by allowing each AI node to be customized and optimized for specific user needs while maintaining the ability to provide general services. Nodes can be configured with specialized models and parameters tailored to individual users or applications, ensuring high precision for specific tasks while preserving overall system versatility through the diverse node population.
3Measurement precision
If AI models are trained with massive amounts of data to improve general performance, then model capability is improved, but resource usage and processing demands increase
Solution Approach 1:
The patent segments the training data and model architecture across multiple distributed nodes, allowing each node to process smaller subsets of data locally. This distributed approach reduces the processing burden on any single node while collectively achieving high AI output quality through aggregated computational power and specialized local models.
4Productivity
If cloud-based AI services handle massive numbers of simultaneous requests, then service capacity is improved, but reliability and predictability deteriorate due to overload and attacks
Solution Approach 1:
The patent divides the service capacity across multiple independent nodes, so that no single point of failure can overwhelm the entire system. Each node handles a portion of the requests independently, providing load distribution and fault tolerance that maintains reliability and predictability even at high service capacities.
Solution Approach 2:
The patent implements feedback mechanisms where nodes monitor their own performance and the overall system state, dynamically adjusting their operational parameters to maintain stability. This self-regulating feedback system prevents overload conditions and maintains predictable service delivery across the distributed network.
5Device complexity
If cloud-based AI services are designed for offline unavailability, then device simplicity is improved, but service availability deteriorates when users are offline
Solution Approach 1:
The patent creates a universal AI node that functions both as a standalone local system and as part of the distributed network. The node maintains full AI capabilities for offline operation while also being able to synchronize and collaborate with other nodes when online, providing service availability across both online and offline states without significantly increasing device complexity.
Data Source
AI summary
A multi-node artificial intelligence topology adapts to service many different overall purposes. Support processing nodes, discriminative AI elements, generative AI elements along with input, output and communication circuitry along with other outside interactions provide the nodal basis for the overall topology. Therewithin, outputs of several nodes drive a single node which uses influence balancing to optimize its own output. Influence is delivered in feed forward and feed back manner. Segmented processing is provided where sections of an overall output goal is processed through the topology in segments, e.g., chapter by chapter of a novel, episode by episode, a full topology processing using internal cross node influence followed by a second full topology processing using both internal cross node and cross segment influence. Pseudo random templating providing constraints used to progress through segments to control an output flow. AI elements can be fully software, use acceleration circuitry, and employ neural network circuitry such as analog and digital versions thereof. Topologies also adapt between local and remote processing locations on a node by node basis, where, for example, some AI elements or nodes operate in the cloud, while other AI elements operate on a particular user's device or other user devices located remotely. Topologies adapt in real time to move nodes to away from a user's device to a cloud counterpart and vice versa as circumstances change.