Attention-Assisted AI Architecture for Dynamic Resource Allocation
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Solution Overview
Problem
Existing AI systems inefficiently utilize computational resources, require costly and time-consuming updates, and create a tradeoff between speed and accuracy when integrated into traditional software platforms.
Innovation Solution
The Attention Assisted AI Architecture (A4 architecture) employs a context module, attention module, specialized processing modules, and decision module to dynamically process data, conserve resources, and adapt to changing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dedicated GPUs and vast storage resources are allocated to AI models, then processing capability and accuracy are improved, but resource waste and cost increase
Solution Approach 1:
The patent implements dynamic resource allocation where the system transitions from static dedicated GPU assignments to dynamic resource sharing. The AI model can allocate computational resources based on actual processing needs, allowing GPUs and storage to be dynamically assigned or released depending on whether AI processing is active, thereby eliminating resource waste while maintaining processing capability when needed.
Solution Approach 2:
The system changes the parameter of resource allocation from fixed/dedicated to variable/dynamic. By implementing parameter changes in the resource management mechanism, the system can adjust the amount of GPU memory and storage allocated to AI models based on actual usage patterns, transforming the rigid resource allocation model into a flexible one that optimizes both performance and resource utilization.
2Reliability
If AI models are retrained and updated frequently to stay relevant, then accuracy and adaptability are improved, but time and cost increase
Solution Approach 1:
The patent implements preliminary action by pre-training AI models with a broad knowledge base and general capabilities before deployment. This preliminary training equips the model with foundational understanding that reduces the need for frequent retraining. The model can adapt to new information and changing conditions through efficient fine-tuning mechanisms rather than requiring complete retraining, thereby maintaining accuracy while reducing update time and computational cost.
3Reliability
If complex special purpose AI models are deployed, then accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent applies segmentation by dividing the AI processing pipeline into distinct stages: a fast general-purpose model for initial processing and a specialized model for accurate analysis of specific data types. The system segments the processing task based on data characteristics, routing different types of data to appropriate processing models. This segmentation allows the system to maintain high processing speed for general data while achieving high accuracy for specialized data processing, resolving the tradeoff between speed and accuracy.
Data Source
AI summary
An apparatus, comprising: a context module to receive data from one or more input sources, to sample the data, and to determine a context of the data; an attention module to receive samples of the data from the context module and determine whether the samples of the data contain information for processing; one or more specialized processing modules to generate processed data from the data based on a determination by the attention module to process the data and to send the processed data to the context module; a decision module to receive the context of the data and the processed data from the context module and determine actions to be executed based on the context of the data and the processed data; and one or more action handlers to execute the actions in response to instructions received from the decision module.