Memory Workload Encoding With RNN for Precise SSD Analysis
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Solution Overview
Problem
Existing memory systems struggle with efficient workload analysis due to the complexity of workload characteristics, which are not adequately captured by existing methods, leading to inefficiencies in performance and reliability, particularly in flash-based storage devices like SSDs.
Innovation Solution
A system utilizing a recurrent neural network (RNN) autoencoder for compact workload representation, comprising an encoder and decoder, to generate a hidden state vector that summarizes input commands, allowing for efficient storage and recovery of workload data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional workload analysis methods are used in memory systems, then the analysis process is simple, but the measurement precision and ability to distinguish workload characteristics is insufficient
Solution Approach 1:
The patent introduces a recurrent neural network coder as an intermediary component between the input workload and the analysis system. This coder transforms raw workload commands into compact representation vectors that capture essential workload characteristics, thereby improving measurement precision without requiring the entire system to become overly complex
Solution Approach 2:
The patent replaces traditional mechanical or algorithmic workload analysis methods with a neural network-based approach. The recurrent neural network coder uses activation functions and hidden state vectors to automatically learn and represent workload features, substituting manual feature engineering and simple counting methods with intelligent pattern recognition
2Measurement precision
If detailed workload analysis is performed to capture all workload characteristics, then the measurement precision improves, but the loss of time and computational overhead increases
Solution Approach 1:
The patent extracts only the most essential workload characteristics and represents them in a compact form using the neural network coder. Instead of analyzing every detail of the workload, the system extracts key features into condensed representation vectors, achieving high measurement precision with reduced time loss
Solution Approach 2:
The recurrent neural network coder performs preliminary processing of workload data by generating compact representation vectors before the main analysis occurs. This preliminary action pre-processes and organizes workload characteristics, making subsequent analysis faster and more efficient
Data Source
AI summary
Compact representation for input workloads is generated in a memory system. The memory system includes a memory device; and a controller including a recurrent neural network coder. The recurrent neural network coder includes an encoder including recurrent encoding blocks. Each recurrent encoding block: receives one of the input commands in an input workload associated with the memory device; and generates a hidden state vector corresponding to the received input command by applying a set of activation functions on the received input command. A last encoding block generates a final hidden state vector as the compact representation vector.