Storage Controller Power Mode Prediction for Latency Reduction
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Solution Overview
Problem
Storage devices face inefficiencies in power mode transitions, leading to increased latency and power consumption due to unpredictable command intervals, as they often switch to low power modes only after receiving a command, rather than anticipating future commands.
Innovation Solution
A storage device with a power mode manager that predicts the timing of future commands using workload information and neural network models, allowing it to switch to a low power mode after completing a command and return to active mode before the next command is received, thereby reducing latency and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If the storage device switches to low power mode after receiving a command, then power consumption is reduced, but latency increases when the next command is received
Solution Approach 1:
The power mode manager performs preliminary actions by predicting the arrival time of the next command using workload information and neural network models. Based on this prediction, the storage device proactively transitions to low power mode at the optimal time, rather than waiting until after a command is received. This preliminary prediction and planning enables the device to minimize both power consumption and latency by being in the ready state exactly when needed.
2Speed
If the storage device operates in active mode continuously, then responsiveness is improved, but power consumption increases
Solution Approach 1:
The storage device dynamically adjusts its power mode based on predicted workload patterns. The power mode manager continuously monitors workload information and uses neural network models to predict future command arrivals, enabling the device to flexibly transition between active and low power modes. This dynamic adaptation allows the system to maintain high responsiveness when commands are expected while reducing power consumption during idle periods, rather than operating in a static continuous active state.
3Use of energy by stationary object
If the storage device uses prediction algorithms, then power mode optimization is improved, but device complexity increases
Solution Approach 1:
The power mode manager acts as an intermediary component that bridges the workload input and power mode control. It collects workload information, processes it through prediction algorithms and neural network models, and based on the predictions, controls the transitions between power modes. This intermediary layer isolates the complexity of prediction algorithms from the rest of the storage device, allowing power optimization through intelligent prediction while keeping the overall device architecture manageable and modular.
Data Source
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AI summary
A storage device is provided. In relation to the storage device operating in an active mode and a low power mode, the storage devices includes a nonvolatile memory including a plurality of nonvolatile memory cells, and a storage controller configured to process commands inputted from a host device in the active mode, wherein the storage controller includes a power mode manager that adjusts the plurality of power modes, wherein, when a first command is inputted, the power mode manager predicts an input prediction time for a second command to be inputted from the host device after the first command, changes from the active mode to the low power mode when a processing operation of the first command is completed, and returns to the active mode from the low power mode when a set return time elapses according to the input prediction time.