Storage Controller Neural Network Log Compression
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Solution Overview
Problem
The increasing capacity and complexity of solid state drives (SSDs) lead to large amounts of dump data being generated during failures, making rapid and accurate debugging challenging due to transmission speed limitations, hindering real-time failure analysis.
Innovation Solution
A storage device and method that compresses log data using neural networks, encoding it based on parameter data and transmitting the encoded data to the host, reducing data size and improving transmission speed while enabling accurate debugging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the SSD collects and transmits large amounts of dump data for detailed failure analysis, then the diagnostic accuracy is improved, but the transmission speed becomes insufficient due to data size limitations
Solution Approach 1:
The patent extracts only the essential and critical information from the dump data using neural network-based encoding. Instead of transmitting the complete raw dump data, the system identifies and transmits only the most relevant features and patterns that are necessary for accurate failure analysis, thereby reducing data size while maintaining diagnostic accuracy.
Solution Approach 2:
The patent inverts the traditional approach by not transmitting the original dump data directly, but rather transmitting an encoded representation that has been processed through neural networks. The encoding transforms the data from its original form into a compressed feature space, and the host system then decodes this information to perform failure analysis.
2Reliability
If the SSD transmits comprehensive dump data to the host, then the failure analysis capability is improved, but the host workload increases due to processing large data volumes
Solution Approach 1:
The patent performs preliminary processing of the dump data at the SSD side before transmission to the host. The neural network encoder pre-processes the raw dump data, extracting essential features and patterns, and prepares the data in a format that requires minimal additional processing at the host side. This preliminary action significantly reduces the computational burden on the host system.
Solution Approach 2:
The patent introduces an intermediary encoding layer using neural networks that transforms the raw dump data into a compressed feature representation. This intermediary process acts as a mediator between data collection and failure analysis, reducing the complexity of data handling at the host while preserving the essential information needed for reliable failure analysis.
3Quantity of substance
If the SSD uses traditional compression methods for log data, then the data size is reduced, but the compression ratio is insufficient for large capacity SSDs
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with a neural network-based encoding system. Instead of using conventional compression techniques that rely on pattern matching and data manipulation, the system uses machine learning models to learn the underlying structure and patterns in the dump data, achieving superior compression ratios that scale with SSD capacity and complexity.
Solution Approach 2:
The patent changes the fundamental parameters of the compression approach by transitioning from fixed algorithmic compression to adaptive neural network encoding. The neural network learns optimal encoding parameters from the data itself, allowing the compression ratio to adapt to the specific characteristics and size of the dump data, thereby achieving higher compression efficiency for large capacity SSDs.
Data Source
AI summary
A storage device and an operating method thereof are provided. The storage device includes a memory configured to store parameter data used as an input in a neural network. The storage device also includes a storage controller configured to receive a request signal from a host. The storage controller is also configured to encode, based on the parameter data, log data in the neural network, the log data indicating contexts of the plurality of components, and transmit the encoded log data to the host.