Memory Controller Segmentation for AI Training Data Prioritization
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Solution Overview
Problem
Current AI model training processes consume excessive computing and memory resources due to the use of all data sets, even when certain data sets have less impact, making it difficult to identify and prioritize higher priority data sets.
Innovation Solution
Implementing a memory controller in a pooled memory subsystem that associates specific data sets with AI models, allowing higher priority data sets to be used for training while omitting lower priority ones, thereby optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all data sets are used for AI model training, then the model can potentially achieve better accuracy, but computing and memory resources are excessively consumed
Solution Approach 1:
The patent segments the training data into different priority levels (first priority, second priority, etc.) based on their impact on model training effectiveness. This segmentation allows the system to selectively process only the most important data segments, thereby reducing overall computing resource consumption while maintaining model accuracy.
Solution Approach 2:
The patent applies local quality by assigning different priority levels to different data sets based on their specific characteristics and impact on model training. High-priority data sets that significantly contribute to model accuracy are processed with higher resource allocation, while low-priority data sets are processed with reduced resources or omitted entirely.
2Ease of operation
If all data sets are stored in memory for training, then data accessibility is improved, but memory consumption increases significantly
Solution Approach 1:
The patent segments training data into priority-based groups and stores only the necessary portions in memory at any given time. High-priority data sets are loaded into memory for active training, while lower-priority data sets are stored externally and loaded only when needed, thereby reducing overall memory consumption while maintaining data accessibility.
Solution Approach 2:
The patent implements dynamic memory allocation where the amount of memory dedicated to training data adjusts based on the current training phase and priority requirements. Memory resources are dynamically allocated to load high-priority data sets during critical training stages and freed when no longer needed, optimizing the balance between accessibility and consumption.
3Productivity
If high-priority data sets are prioritized for training, then training efficiency is improved, but it becomes difficult to identify which data sets have higher priority
Solution Approach 1:
The patent performs preliminary analysis and classification of data sets before the actual model training begins. During this preliminary phase, the system evaluates each data set's potential impact on model training and assigns priority levels in advance. This preliminary action eliminates the difficulty of identifying priority during training, as the classification is already established.
Solution Approach 2:
The patent implements a feedback mechanism where the system monitors the actual impact of different data sets on model training performance and adjusts priority assignments accordingly. This feedback loop helps accurately identify which data sets truly have higher priority based on their actual contribution to model improvement, resolving the difficulty of initial priority identification.
Data Source
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AI summary
Technology for a memory controller is described. The memory controller can receive a request to store training data. The request can include a model identifier (ID) that identifies a model that is associated with the training data. The memory controller can send a write request to store the training data associated with the model ID in a memory region in a pooled memory that is allocated for the model ID. The training data that is stored in the memory region in the pooled memory can be addressable based on the model ID.