AI-Driven Cache Memory Structure Design for Accelerator Workloads
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Solution Overview
Problem
Existing methods for designing cache memory structures in accelerators, particularly for artificial intelligence applications, are time-consuming and costly, relying heavily on human intuition rather than optimized design principles, leading to suboptimal performance and increased development time.
Innovation Solution
A method and apparatus that utilize artificial intelligence to automatically design cache memory structures by extracting memory access patterns and applying a trained cache structure design model to derive an optimal cache memory configuration, considering hybrid cache memory structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human intuition and experience are used to design cache memory structures, then design flexibility and adaptability are maintained, but design time and cost increase significantly
Solution Approach 1:
The system enables self-service design by allowing the cache memory structure to be automatically configured based on application characteristics and memory access patterns. The AI-based automated design system performs the design tasks autonomously without requiring extensive human intervention, thereby reducing design time while maintaining optimization quality.
Solution Approach 2:
The patent replaces the mechanical system of manual human design with an automated AI-based design system. The machine learning model automatically analyzes application data, extracts memory access patterns, and generates optimized cache configurations, substituting human intuition and experience with computational algorithms that can process large datasets efficiently.
2Device complexity
If manual cache memory design methods are used, then design complexity can be managed through human expertise, but productivity and development speed are reduced
Solution Approach 1:
The system incorporates feedback mechanisms where the AI model continuously analyzes application execution data, memory access patterns, and performance metrics to iteratively refine and optimize cache memory configurations. This feedback loop enables the system to adapt to complex design requirements automatically, maintaining high productivity while managing design complexity through data-driven optimization.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting cache memory configuration parameters such as capacity, associativity, and replacement policies based on extracted memory access patterns and application characteristics. The AI model automatically modifies these parameters to optimize performance, enabling rapid development speed without sacrificing the ability to manage complex design requirements.
3Loss of time
If automated AI-based design is implemented, then design time and cost are reduced, but the system complexity increases
Solution Approach 1:
The automated design system is segmented into distinct functional modules: data collection module for gathering application execution data, pattern extraction module for analyzing memory access patterns, AI modeling module for generating optimized configurations, and validation module for verifying design quality. This segmentation reduces overall system complexity by breaking down the complex design process into manageable, independent components that can be developed and maintained separately.
4Adaptability or versatility
If general cache memory design approaches are used, then broad applicability is maintained, but optimization for specific applications is insufficient
Solution Approach 1:
The system employs dynamics by creating a flexible, adaptive design approach where the AI model dynamically adjusts cache memory configurations based on the specific characteristics of each application. The system can adapt to different application types, workloads, and memory access patterns, providing optimized solutions for each specific case while maintaining the capability to handle diverse applications through a unified framework.
Data Source
AI summary
Disclosed are a method for designing a cache memory structure of an artificial intelligence accelerator and an apparatus therefor. A method for designing a structure of a cache memory of an accelerator in a cache memory structure designing apparatus is a cache memory structure designing method including a memory access information extracting step of extracting a memory address of a cache memory accessed by a processing element array (PE array) at every time stamp for an application input to the accelerator, a memory access pattern determining step of determining a memory access pattern for the application based on the memory addresses of the cache memory accessed over time, and a cache structure design step of deriving a cache memory structure using a cache structure design model trained in advance based on a memory access pattern and generating cache structure design information for the cache memory structure.