Neural Network Task Manager Polling Time Prediction
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Solution Overview
Problem
Existing neural network systems face inefficiencies in calculating and predicting polling times for calculation results, leading to suboptimal performance in deep learning processes.
Innovation Solution
A neural network system that includes a task manager to calculate and predict polling times for resources performing calculation operations, allowing for accurate timing of result polling through the use of a calculation processing graph and subgraphs allocated to suitable resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If polling is performed frequently to ensure timely retrieval of calculation results, then response time is improved, but system overhead and resource consumption increase
Solution Approach 1:
The system performs preliminary calculation of polling time based on graph computation characteristics and resource performance parameters before actual polling occurs. This pre-computation enables the system to determine optimal polling intervals in advance, avoiding the need for frequent trial-and-error polling while ensuring results are retrieved at the most efficient moment.
Solution Approach 2:
The system implements a feedback mechanism where polling time calculations are continuously refined based on actual resource performance data and computation graph characteristics. The task manager uses feedback from previous polling operations to adjust and optimize future polling intervals, improving efficiency while reducing unnecessary system overhead.
2Productivity
If complex calculation graphs are processed using a unified approach, then system simplicity is maintained, but processing efficiency decreases
Solution Approach 1:
The system segments the calculation processing into distinct components: graph computation analysis, polling time calculation, and result retrieval. By dividing the complex processing task into manageable segments with specialized handling for each, the system achieves high processing efficiency without requiring overly complex unified infrastructure.
Solution Approach 2:
The task manager serves multiple functions within a single component: it manages computation graphs, calculates optimal polling times, coordinates resource allocation, and handles result retrieval. This multi-functionality allows the system to maintain relative simplicity while achieving sophisticated processing efficiency through a versatile central coordinator.
Data Source
AI summary
A neural network system for predicting a polling time and a neural network model processing method using the neural network system are provided. The neural network system includes a first resource to generate a first calculation result obtained by performing at least one calculation operation corresponding to a first calculation processing graph and a task manager to calculate a first polling time taken for the first resource to perform the at least one calculation operation and to poll the first calculation result from the first resource based on the calculated first polling time.