Data Storage Device Cache Optimization via Neural Network Prediction
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Solution Overview
Problem
Current data storage devices in autonomous vehicles lack efficient predictive maintenance capabilities, relying on insufficient sensor data analysis that does not account for personalized operating environments and habits, leading to potential component failures during vehicle operation.
Innovation Solution
A data storage device equipped with an artificial neural network (ANN) that collects and analyzes sensor data to predict maintenance needs by training on specific environmental and operational patterns, using both supervised and unsupervised machine learning techniques to identify anomalies and schedule maintenance services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensor data analysis is used for maintenance prediction, then the system is simple to implement, but the prediction accuracy and reliability are insufficient
Solution Approach 1:
The system performs preliminary training of the neural network model using historical sensor data and maintenance records before actual deployment. This preliminary action enables the system to learn patterns and relationships in advance, improving prediction reliability when the model is deployed in the vehicle without requiring complex real-time processing.
Solution Approach 2:
The patent introduces an external server as an intermediary that handles the complex tasks of data collection, model training, and model updates. The server acts as a mediator between the simple in-vehicle prediction system and the complex training infrastructure, allowing the vehicle system to remain relatively simple while achieving high prediction reliability through the server's processing capabilities.
2Measurement precision
If comprehensive sensor data collection is implemented to capture personalized operating environments, then the prediction accuracy improves, but the data storage and processing requirements increase
Solution Approach 1:
The system extracts only the essential features and patterns from the comprehensive sensor data that are most relevant to maintenance prediction. Instead of storing and processing all raw sensor data, the neural network extracts key characteristics such as operating patterns, environmental conditions, and usage behaviors, significantly reducing the data volume while maintaining prediction accuracy.
Solution Approach 2:
The patent applies different processing strategies to different types of sensor data based on their relevance to maintenance prediction. Critical sensors that provide information about component stress and usage patterns are processed in detail, while less relevant sensors are processed at a higher level or aggregated, optimizing the balance between measurement precision and data quantity.
3Speed
If real-time neural network processing is performed in the vehicle, then the maintenance prediction responsiveness improves, but the computational power and energy consumption increase
Solution Approach 1:
The neural network model is pre-trained and optimized on an external server before being deployed to the vehicle. This preliminary training action creates a compact, optimized model that can run efficiently in the resource-constrained vehicle environment, achieving fast real-time predictions without requiring excessive computational power or energy consumption during actual operation.
4Adaptability or versatility
If the system adapts to personalized operating habits and environments, then the prediction accuracy improves, but the model complexity and training requirements increase
Solution Approach 1:
The system performs preliminary adaptation by training the neural network on diverse historical data representing various operating environments and user habits before deployment. This preliminary action enables the model to learn and adapt to different patterns in advance, allowing it to handle personalized operating conditions without requiring complex real-time adaptation mechanisms in the vehicle.
Solution Approach 2:
The system implements feedback mechanisms where prediction results and actual maintenance outcomes are continuously monitored and fed back to the external server. The server uses this feedback to refine and update the neural network model, improving adaptability to personalized operating habits over time while keeping the in-vehicle system relatively simple.
Data Source
AI summary
Systems, methods and apparatus of intelligent optimization of caching operations in a data storage device. For example, the data storage device can include: one or more storage media components; a controller configured to store data into and retrieve data from the one or more storage media components; a cache memory configured to cache data that is stored in the one or more storage media components; and an artificial neural network configured to receive, as input and as a function of time, operating parameters indicative a data access pattern. The artificial neural network generates, based on the input, a prediction to determine an optimized set of cache configuration parameters; and cache operations of the cache memory are configured according to the optimized set of cache configuration parameters determined based on the prediction.