AI Cache Memory Design Training Data Generation
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Solution Overview
Problem
Existing methods for designing cache memory structures are labor-intensive and inefficient, requiring manual consideration of complex hardware components and diverse application requirements, which leads to suboptimal performance and increased energy consumption.
Innovation Solution
A method for generating training data for an artificial intelligence model that automatically designs cache memory structures, involving the setting of a reuse profile, selection of reuse distances, and modification of the profile based on real reuse distances, to optimize cache performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual design methods are used for cache memory structures, then developers can directly control design parameters, but the design process becomes labor-intensive and inefficient
Solution Approach 1:
The patent replaces manual mechanical design processes with an automated AI-based system. The cache memory design is transformed from a manual iterative process into an automated optimization process using machine learning models that take application characteristics as input and generate optimized cache configurations, eliminating the need for developers to manually adjust numerous parameters.
Solution Approach 2:
The AI model enables the cache memory design system to optimize itself automatically. The system self-adjusts cache parameters by learning from training data and application characteristics, performing self-optimization without requiring manual intervention from developers, thereby improving design productivity while maintaining control over design parameters.
2Loss of time
If automated design tools are used to respond to latest technologies, then development cycles are shortened, but the complexity of generating accurate training data increases
Solution Approach 1:
The patent applies preliminary action by pre-generating comprehensive training data that captures diverse application characteristics and cache access patterns before the actual design process. This pre-prepared training data includes various reuse distance scenarios and application types, enabling the AI model to be trained in advance and ready for rapid deployment, thus shortening development cycles while managing data generation complexity through systematic preprocessing.
Solution Approach 2:
The system manages training data complexity by systematically varying key parameters such as reuse distances, cache line sizes, and associativity levels during data generation. By controlling and parameterizing the complexity through defined variable ranges and combinations, the system can generate comprehensive training data without becoming unmanageably complex, enabling efficient automated design.
3Measurement precision
If comprehensive training data is generated with multiple reuse distances, then AI model accuracy improves, but the data generation process becomes more complex
Solution Approach 1:
The patent segments the training data generation process into distinct components: generating access patterns with specific reuse distances, creating corresponding cache traces, and labeling with performance metrics. By dividing the complex data generation task into manageable segments that can be systematically combined, the system achieves high AI model accuracy through comprehensive data coverage while keeping the generation process organized and controllable.
Data Source
AI summary
Proposed is a method for generating training data for training an artificial intelligence model capable of automatically designing a cache memory structure. The method for generating training data for cache memory design includes setting a reuse profile, setting a first selection reuse distance based on the reuse profile, setting a first load index and a first real reuse distance based on the first selection reuse distance, modifying the reuse profile according to setting the first real reuse distance, and setting a second selection reuse distance based on the modified reuse profile.