Memory Subsystem Power State Transition Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional memory sub-systems face inefficiencies in power state transitions, leading to increased power consumption, thermal issues, and reduced endurance due to frequent transitions to deep idle states, which negatively impact command response time and input/output performance.
Innovation Solution
A power state transition management component is introduced to establish a transitory idle state with shorter entry and exit latencies, reducing energy expenditure and thermal consumption by maintaining the memory sub-system in this state for an optimized duration before transitioning to a deep idle state, thereby avoiding frequent writes to non-volatile memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If the memory sub-system transitions to a deep idle state to reduce power consumption, then power consumption is reduced, but entry and exit latency increase
Solution Approach 1:
The patent segments the idle state into multiple levels: a shallow idle state with shorter latency and a deep idle state with lower power consumption. The system dynamically transitions between these segmented states based on workload patterns, allowing optimization of both power consumption and latency for different operational scenarios.
Solution Approach 2:
The patent implements dynamic power state management where the memory sub-system adapts its power state transitions based on observed workload patterns and idle duration predictions. This dynamic approach allows the system to choose between shallow and deep idle states optimally, balancing power savings against latency requirements in real-time.
2Use of energy by stationary object
If the memory sub-system transitions to a deep idle state to minimize power consumption, then power consumption is reduced, but command response time deteriorates
Solution Approach 1:
The patent uses machine learning models to predict idle duration in advance. When a short idle period is predicted, the system performs preliminary actions by transitioning only to a shallow idle state, avoiding the deeper transition that would cause latency issues. This preliminary prediction-based action prevents command response time deterioration while still achieving some power savings.
Solution Approach 2:
The system dynamically adjusts its power state strategy based on predicted workload patterns. For anticipated short idle periods, it maintains a more responsive shallow idle state. For predicted long idle periods, it transitions to the power-saving deep idle state. This dynamic adaptation optimizes command response time according to actual operational needs.
3Use of energy by stationary object
If the memory sub-system transitions to a deep idle state to reduce power consumption, then power consumption is reduced, but IO performance deteriorates
Solution Approach 1:
The patent segments the idle state hierarchy into shallow and deep levels, each with different performance characteristics. By selectively transitioning to only the shallow idle state when IO activity is anticipated soon, the system maintains better IO performance while still achieving partial power savings, avoiding the performance penalty of deep idle transitions.
Solution Approach 2:
The system employs machine learning models that continuously learn from actual IO workload patterns and power state transition outcomes. This feedback mechanism allows the system to refine its predictions and optimize the choice between shallow and deep idle states, improving IO performance by avoiding unnecessary deep transitions that would harm productivity.
4Use of energy by stationary object
If the memory sub-system frequently transitions to a deep idle state to save power, then power consumption is reduced, but endurance of non-volatile memory decreases
Solution Approach 1:
The patent applies partial action by transitioning to a shallow idle state that provides intermediate power savings without the full impact on memory endurance. This partial transition achieves some power reduction while avoiding the excessive wear on non-volatile memory components that results from frequent deep idle state transitions.
Solution Approach 2:
The system dynamically optimizes the balance between power consumption and memory endurance by using machine learning to predict actual idle durations. This dynamic approach prevents unnecessary deep idle transitions that would harm endurance, while still achieving adequate power savings through selective shallow idle transitions.
Data Source
AI summary
A duration of time for a first idle state of a memory sub-system is determined, where the memory sub-system includes an active state and a second idle state. A first command is received to transition from the active state to the second idle state. In response to the first command, the memory sub-system is transitioned to the first idle state. The memory sub-system is transitioned from the first idle state to the second idle state in response to an expiration of the duration of time.