Federated Learning for Memory Device Aging Prediction
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Solution Overview
Problem
Current methods for predicting the mean time to failure (MTTF) of memory devices are often expensive and time-consuming, involving in-lab evaluations, and lack efficient decentralized solutions for aging prediction and data privacy.
Innovation Solution
The use of memory-side channel analysis for federated learning, where machine learning models are trained on memory usage and device characteristics at different tiers, aggregated to predict MTTF without sharing private data, enabling decentralized training and deployment across customer platforms at varying price points based on specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-lab evaluations are used to predict MTTF of memory devices, then prediction accuracy is improved, but cost and time consumption increase
Solution Approach 1:
The patent replaces physical in-lab evaluation mechanisms with a computational machine learning model that processes memory usage data and device characteristics to predict MTTF. This substitution eliminates the need for time-consuming physical testing while maintaining prediction accuracy through algorithmic analysis of operational patterns.
Solution Approach 2:
The patent performs preliminary data collection and model training during normal device operation, gathering memory usage data and device characteristics in advance. This preliminary action enables the ML model to be ready for deployment without requiring subsequent in-lab evaluation, thus reducing time consumption while maintaining accuracy.
2Measurement precision
If in-lab evaluations are used to predict MTTF of memory devices, then prediction accuracy is improved, but cost increases
Solution Approach 1:
The patent replaces expensive in-lab evaluation infrastructure with a software-based machine learning solution that runs on existing computing devices. This substitution eliminates the need for specialized laboratory equipment and personnel, significantly reducing costs while maintaining prediction accuracy through computational analysis.
Solution Approach 2:
The patent creates a virtual copy of the evaluation process through a machine learning model that replicates the predictive capabilities of in-lab evaluations without requiring physical testing. This digital copy enables accurate MTTF prediction at a fraction of the cost of physical evaluations.
3Measurement precision
If centralized data collection is used for training ML models, then model accuracy is improved, but data privacy is compromised
Solution Approach 1:
The patent segments the centralized training process into distributed local training units across multiple devices. Each device trains its own ML model locally using its private data, then only model parameters (not raw data) are shared and aggregated. This segmentation maintains data privacy while achieving model accuracy through collective learning from distributed data sources.
Solution Approach 2:
The patent introduces federated learning as an intermediary mechanism that enables collaborative model training without direct data sharing. The intermediary aggregates model updates from multiple devices while preserving the privacy of individual device data, thus maintaining both data privacy and model accuracy through indirect information exchange.
Data Source
AI summary
Methods and systems associated with a machine learning model aggregation are described. A system can include a first computing device, a second computing device, a local federated server, and a global federated server. The first computing device and the second computing device can train respective first and second machine learning models based on gathered memory usage data and device characteristic data associated with a respective first plurality of memory devices and second plurality of memory devices. The local federated server can aggregate the first machine learning model and the second machine learning model into a third machine learning model. The global federated server can aggregate the third machine learning model with a fourth machine learning model comprising a plurality of aggregated machine learning models into a fifth machine learning model and predict aging of the first plurality of memory devices and the second plurality of memory devices.