Flash Controller QoS Neural Network Latency Prediction
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Solution Overview
Problem
Fixed, deterministic Quality of Service (QoS) in Solid State Drives (SSDs) can be estimated during design, but variable, probabilistic QoS factors, such as non-deterministic user workloads and NAND operations, affect drive performance, leading to latency issues that exceed maximum allowed latency thresholds.
Innovation Solution
A method and apparatus using a Quality of Service (QoS) neural network in the flash controller to predict latency by receiving configuration files with weight and bias values, identifying feature values for commands in the instruction queue, and modifying or rescheduling commands to maintain QoS within predetermined tolerance requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed, deterministic QoS estimation methods are used during design, then design-time QoS prediction is improved, but runtime variable, probabilistic QoS factors cannot be addressed
Solution Approach 1:
The neural network model is trained in advance using simulations or emulations to learn patterns of QoS degradation under various workload conditions. The trained model is then deployed in the flash controller to predict latency in real-time, combining preliminary learning with runtime adaptation.
Solution Approach 2:
The patent replaces traditional deterministic QoS estimation mechanisms with a neural network-based probabilistic prediction system. This substitution enables the system to handle variable, non-deterministic workload patterns that cannot be addressed by fixed design-time estimation methods.
2Speed
If commands are processed in the instruction queue without modification, then processing speed is maintained, but latency may exceed maximum allowed thresholds
Solution Approach 1:
The neural network continuously monitors command characteristics and provides feedback predictions about potential latency. Based on these predictions, the system dynamically adjusts command processing by modifying or rescheduling commands that are predicted to cause QoS violations, creating a closed-loop control system.
Solution Approach 2:
The system transitions from static, fixed QoS estimation to dynamic, real-time probabilistic prediction. The neural network adapts its predictions based on current workload conditions, and the command queue management dynamically adjusts processing order based on predicted latency, enabling the system to maintain QoS compliance while preserving processing speed.
3Reliability
If variable, probabilistic QoS factors are addressed in real-time, then QoS compliance is improved, but device complexity increases
Solution Approach 1:
Instead of implementing a completely new complex QoS management system, the patent uses a neural network model that copies and learns from existing workload patterns and command structures. The model predicts QoS outcomes by analyzing familiar command characteristics, reducing the need for complex real-time optimization algorithms.
Solution Approach 2:
The system manages complexity by changing parameters of command processing rather than fundamentally altering the controller architecture. The neural network predicts latency based on input parameters like command type, queue depth, and workload patterns, and the system responds by adjusting processing parameters such as command scheduling priority, maintaining QoS compliance with minimal architectural changes.
Data Source
AI summary
A method for meeting quality of service (QoS) requirements in a flash controller that includes one or more instruction queues and a neural network engine. A configuration file for a QoS neural network is loaded into the neural network engine. A current command is received at the instruction queue(s). Feature values corresponding to commands in the instruction queue(s) are identified and are loaded into the neural network engine. A neural network operation of the QoS neural network is performed using as input the identified feature values to predict latency of the current command. The predicted latency is compared to a first latency threshold. When the predicted latency exceeds the first latency threshold one or more of the commands in the instruction queue(s) are modified. The commands are not modified when the predicted latency does not exceed the latency threshold. A next command in the instruction queue(s) is then performed.