Transformer Loop Exit Mechanism for AI Latency Reduction
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Solution Overview
Problem
Existing AI learning models face high computational costs, increased latency and processing time, and elevated power consumption due to their architecture.
Innovation Solution
The technique involves a loop exit mechanism that uses interactions between a system scheduler and a processing unit scheduler to determine a termination condition, reducing iterations in transformer processing layers by establishing a threshold based on application and environment specifics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the transformer model processes all iterations in processing layers, then the accuracy and completeness of AI learning results are improved, but the computational cost and processing time increase significantly
Solution Approach 1:
The patent implements dynamic iteration termination by allowing the processing layers to exit iterations early based on runtime conditions. The system monitors convergence criteria and stops processing when sufficient accuracy is achieved, rather than executing a fixed number of iterations. This dynamic approach adapts the processing duration to the actual needs of each computation, reducing unnecessary processing time while maintaining accuracy.
Solution Approach 2:
The patent employs feedback mechanisms where the system continuously monitors the output of processing layers and compares it against convergence thresholds. When the feedback indicates that the results have converged or met the required accuracy level, the system terminates further iterations. This feedback-driven approach ensures that processing stops at the optimal point, avoiding both insufficient processing and excessive computation.
2Measurement precision
If the transformer model executes complete iterative procedures, then the quality of generated content is improved, but the computational resources and power consumption increase
Solution Approach 1:
The patent applies partial action by executing only the necessary portion of iterative procedures required to achieve acceptable content quality. Instead of always completing the full predetermined number of iterations, the system performs partial processing when convergence is achieved earlier, thereby reducing power consumption while maintaining sufficient quality for practical applications.
3Device complexity
If the system uses a fixed number of iterations in processing layers, then the computational simplicity is maintained, but the response time and adaptability to different applications are reduced
Solution Approach 1:
The patent transforms the static iteration count into a dynamic parameter that adapts to different applications and data characteristics. The system uses application-specific configuration to set baseline iteration numbers but allows runtime adjustments based on convergence monitoring. This dynamic adaptability enables the same computational framework to efficiently handle diverse applications without requiring complex reconfiguration.
Data Source
AI summary
A system and a method for loop exit are disclosed. A unit scheduler includes a command queue, a report queue, an interface, and a unit controller. The command queue is configured to store commands for execution in an iterative procedure for an application using a transformer model with a multi-head attention (MHA) mechanism and a decoder. The report queue is configured to store status reports on the execution of the commands. The interface is configured to communicate with a host processor to receive the commands and to transmit the status reports. The unit controller is configured to determine a change of the iterative procedure based on a loop exit condition being met. The unit controller reports the loop exit condition in the report queue.