Power-Optimizing Memory Analyzer for Dynamic Power State Management
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Solution Overview
Problem
Current memory systems in portable electronic devices face challenges in optimizing power consumption while maintaining performance and reliability, particularly due to increased complexity and shrinking sizes, which lead to issues like high memory access power, standby power increase at elevated temperatures, and potential loss of system integrity when powering down memory.
Innovation Solution
A power-optimizing memory analyzer is introduced, which includes a task database and an allocation module that determines the optimal grouping of memory blocks based on power reduction parameters, along with a power profiling module to generate run-time power profiles for efficient power state management, ensuring quality of service and reducing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If memory blocks are kept in active state to maintain system performance, then system reliability and response time are improved, but power consumption increases
Solution Approach 1:
The patent segments the memory array into multiple memory blocks that can be independently powered down. Instead of keeping the entire memory array active, only the necessary blocks are activated based on task requirements, allowing other blocks to enter low-power states while maintaining system functionality.
Solution Approach 2:
The patent implements dynamic memory power management where the power state of memory blocks is changed at runtime based on actual task needs. The system transitions between different power states (active, retention, power-down) dynamically, allowing optimization of power consumption while maintaining performance when needed.
2Use of energy by moving object
If memory blocks are powered down to reduce power consumption, then energy efficiency is improved, but system response time and reliability deteriorate
Solution Approach 1:
The patent uses preliminary action by keeping frequently accessed memory blocks in retention state (a intermediate state between power-down and active) or pre-activating blocks that are likely to be needed soon. This allows the system to maintain quick response times for common operations while still achieving power savings from less frequently used blocks.
Solution Approach 2:
The patent implements feedback mechanisms where the system monitors memory access patterns and task requirements to dynamically adjust which memory blocks remain active or in retention state. This feedback loop allows optimization of the balance between power savings and response time based on actual system behavior.
3Productivity
If more memory blocks are kept active to handle increased computing power requirements, then system performance is improved, but power consumption and device complexity increase
Solution Approach 1:
The patent divides the memory into multiple independently controllable blocks, allowing the system to activate only the necessary portion of memory for current computing tasks. This segmentation enables the system to provide high computing power when needed while minimizing overall power consumption by keeping unused memory blocks in low-power states.
4Use of energy by moving object
If memory blocks are grouped differently to optimize power reduction, then power consumption is reduced, but system complexity in managing memory allocation increases
Solution Approach 1:
The patent performs preliminary action by pre-calculating and pre-grouping memory blocks based on their access patterns and power characteristics. The system uses this pre-organization to quickly allocate and de-allocate memory blocks without complex real-time calculations, reducing the computational overhead of memory management while achieving optimal power reduction.
Data Source
AI summary
Embodiments of the present disclosure provide a power-optimizing memory analyzer, a method of operating a power-optimizing memory analyzer and a memory system employing the analyzer or the method. In one embodiment, the power-optimizing memory analyzer is for use with an array of memory blocks and includes a task database configured to provide a parameter set corresponding to each of a set of tasks to be performed in a system. The power-optimizing memory analyzer also includes an allocation module configured to determine offline, a group of memory blocks in the array corresponding to the parameter set for each task and based on providing a power reduction for the array. The power-optimizing memory analyzer further includes a power profiling module configured to generate run-time power profiles of memory power states for each task allowing transparent and dynamic control of the memory power states while maintaining a required quality of service.