Processor Component Configuration Using Invariant Statistics

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Solution Overview

Problem

Existing methods for configuring hardware processor components, such as prefetchers and branch predictors, are inefficient as they rely on performance metrics that change with different applications, leading to suboptimal performance and complex retraining when components are replaced or modified.

Innovation Solution

Configuring hardware processor components using invariant statistics that are independent of performance metrics, utilizing machine learning models trained on invariant statistics to dynamically adjust settings based on application execution patterns, eliminating the need for retraining when components are replaced or modified.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If performance metric-based configuration methods are used to optimize hardware processor components, then component performance can be improved for specific applications, but the system requires complex retraining when components are replaced or modified and cannot maintain consistent performance across different applications

Engineering Contradiction:

Improveprocessor performanceVSAvoidconfiguration complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent changes the fundamental parameter used for configuration from performance metrics (which vary with application and component) to invariant statistics (which remain consistent). This allows the same configuration settings to be applied across different applications and component instances without retraining, resolving the contradiction between performance optimization and configuration complexity

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent creates a universal configuration approach using invariant statistics that can be applied to any component instance regardless of the specific application or component version. The configuration derived from invariant statistics is universally applicable across different prefetcher implementations and applications, eliminating the need for application-specific or component-specific retraining

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Measurement precision

If performance metrics are used to train machine learning models for component configuration, then configuration can be optimized for measured performance, but the training data changes with every component replacement or modification requiring retraining

Engineering Contradiction:

Improveconfiguration accuracyVSAvoidcomponent replaceability

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent extracts the training basis from performance metrics (which are tied to specific components and applications) and replaces it with invariant statistics (which are independent of specific components). This extraction allows the machine learning model to be trained once on invariant statistics and then applied universally to any component without requiring retraining when components are replaced or modified

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

Instead of training on performance metrics that change with component variations (the traditional approach), the patent inverts the approach by training on invariant statistics that remain constant. This inversion makes the training data independent of component identity, allowing the same trained model to work with any component instance

Inventive Principle:

Principle #13The other way round (Inversion)

3Productivity

If application-specific configuration settings are applied to hardware processor components, then performance can be optimized for individual applications, but the configuration becomes ineffective when the same component handles different applications

Engineering Contradiction:

Improveapplication performanceVSAvoidapplication independence

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent changes the basis of configuration from application-specific performance metrics to application-independent invariant statistics. This parameter change enables the configuration to be derived once from invariant statistics and then applied consistently across all applications, achieving both performance optimization and application independence simultaneously

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12386624B2Invariant statistics-based configuration of processor components

Publication Date: 2025.08.12 ADVANCED MICRO DEVICES INC

AI summary

Techniques are described for a hardware processor to dynamically configure a component that improves a processor function with a configuration setting based on invariant statistics. The invariant statistics are generated by execution of the instructions from one or more applications and are independent of the performance metrics of the processor function for the execution. In an embodiment, the configuration setting for the component is generated using a machine learning model.