Memory Subsystem Firmware Tuning for Confidential Workloads

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Solution Overview

Problem

Customers are reluctant to share customer-specific workloads due to privacy, security, and competitive advantage concerns, making it difficult for manufacturers to optimize memory sub-systems to meet target performance metrics.

Innovation Solution

A memory sub-system optimization device that generates custom firmware based on customer-specific workloads using machine learning algorithms, optimizing parameters to meet target performance metrics while preserving confidentiality.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If manufacturers request customer-specific workloads for optimization, then memory sub-system performance can be optimized to meet target metrics, but customer privacy and security concerns prevent workload sharing

Engineering Contradiction:

Improveperformance optimization precisionVSAvoidworkload confidentiality

Core Design Contradiction:

Manufacturing precisionVSLoss of information

Solution Approach 1:

The patent introduces an optimization device as an intermediary between customers and manufacturers. This device receives workloads from customers, performs optimization analysis locally, and generates optimized parameter sets without requiring customers to directly share sensitive workload data with manufacturers. The intermediary preserves customer confidentiality while enabling performance optimization.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The optimization device creates copies of workload characteristics and performance metrics for analysis purposes, rather than requiring customers to share original sensitive workloads. These copies contain sufficient information for optimization while preserving the confidentiality of the actual customer workloads.

Inventive Principle:

Principle #26Copying

2Ease of manufacture

If default parameter sets are used for all customers, then device complexity is reduced and ease of manufacture is improved, but performance optimization for specific workload scenarios is lost

Engineering Contradiction:

Improvefirmware generation easeVSAvoidworkload-specific optimization

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The system transitions from static default parameter sets to dynamic, workload-adaptive parameter sets. The optimization device analyzes specific workload characteristics and generates customized parameter sets tailored to each customer's needs, enabling the firmware to adapt dynamically to different workload scenarios while maintaining ease of manufacture through automated generation processes.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent employs parameter changes by adjusting firmware parameters based on workload analysis. The optimization device modifies parameter values (such as cache sizes, buffer allocations, and operation scheduling) to optimize performance for specific workload types while maintaining a standardized base firmware structure for ease of manufacture.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If custom firmware is generated for each customer, then performance metrics can be optimized for specific workloads, but device complexity and optimization process time increase

Engineering Contradiction:

Improveperformance metric achievementVSAvoidoptimization device complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The optimization device is designed as a universal platform that can handle multiple workload types and generate optimized parameter sets for various scenarios using a single device architecture. This multi-functionality reduces the need for multiple specialized devices while maintaining the ability to optimize for different customer needs.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The optimization device performs automated analysis and generates optimized parameter sets without requiring extensive manual intervention. The system self-services by automatically analyzing workload characteristics, determining optimal parameters, and generating customized firmware configurations, thereby reducing operational complexity despite the customization process.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260030017A1Machine learning driven device for optimizing memory sub-systems

Publication Date: 2026.01.29 MICRON TECHNOLOGY INC

AI summary

A system including a plurality of memory sub-systems and an optimization device coupled to the plurality of memory sub-systems. At least one live customer-specific workload is received by the optimization device. A subset of the plurality of memory sub-systems is caused to run the at least one live customer-specific workload. Optimized parameter values associated with the subset of the plurality of memory sub-systems for the at least one live customer-specific workload is obtained. A firmware image for the subset of the plurality of memory sub-systems is generated based on the optimized parameter values.