Deep Learning Model Calibration Layer Grouping
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Solution Overview
Problem
Current deep learning model calibration methods are inefficient due to limited memory resources, requiring sequential calibration of layers and leading to suboptimal memory usage and prolonged calibration times, as different layers have varying memory requirements.
Innovation Solution
A method that determines layer attribute information and groups to-be-calibrated layers based on available resources, prioritizing layers with the greatest resource requirements and optimizing group allocation to balance resource usage and minimize the number of calibration operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If all to-be-calibrated layers are calibrated in sequence according to data transmission direction with fixed grouping, then the calibration process is simple to implement, but the memory resources are not efficiently utilized and calibration time is prolonged
Solution Approach 1:
The patent applies dynamic grouping by sorting layers according to their memory requirements and dynamically adjusting the grouping configuration based on available memory resources. Instead of fixed sequential grouping, the system recalculates optimal groupings that adapt to resource constraints, thereby improving memory utilization and reducing calibration time while maintaining implementation feasibility.
2Device complexity
If layers are grouped without considering memory requirements, then resource allocation is simple, but memory resources are wasted and calibration operations increase
Solution Approach 1:
The patent changes the grouping parameter from simple sequential assignment to memory-aware grouping. By considering the memory requirements of each layer as a key parameter, the system optimizes group configurations to better match available resources, reducing memory waste and minimizing the number of calibration operations needed.
3Reliability
If sequential calibration of all layers is performed, then all layers are calibrated completely, but the calibration time becomes excessively long due to limited memory
Solution Approach 1:
The patent segments the calibration process into multiple optimized groups based on memory requirements rather than processing all layers sequentially. By dividing layers into strategically formed groups that fit within memory constraints, the system maintains complete calibration coverage while significantly reducing total calibration time through parallelizable group operations.
Data Source
AI summary
A calibration method, a calibration apparatus, a terminal device and a storage medium are provided. The method comprises the following steps: determining layer attribute information of each to-be-calibrated layer in a model (S110); and determining the group in which each of the to-be-calibrated layers is located according to the total available resources and the layer attribute information of each of the to-be-calibrated layers (S120). The layer attribute information of any of the to-be-calibrated layers comprises layer required resources, the layer required resources are resources needing to be occupied when the to-be-calibrated layer is calibrated; and the total available resources are the total resources used for calibration. By means of the method, all of to-be-calibrated layers can be reasonably grouped on the premise that the total available resources can provide support, so that the layer required resources in each calibration operation are balanced and large as much as possible, thereby making full use of resources. Moreover, the number of calibration operations is reduced, and the calculation speed during the calibration of a model is increased.