Granular Memory Refresh Control for DRAM Power Optimization
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Solution Overview
Problem
Current memory refresh mechanisms in DRAM devices consume significant power and degrade system performance, as they often use uniform refresh intervals that are not optimized for different bit positions within a word, leading to inefficiencies in power usage and potential data loss due to retention failures.
Innovation Solution
A method and system that dynamically determine and adjust refresh intervals for each bit position in a memory device based on their relative importance, using metrics like fidelity (e.g., MSE, PSNR) and resource (e.g., refresh power) metrics, with the help of convex programming and machine learning techniques to minimize errors and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If uniform refresh intervals are used for all bit positions, then data retention is maintained across all bits, but power consumption increases and system performance degrades
Solution Approach 1:
The patent applies local quality by assigning different refresh intervals to different bit positions based on their relative importance to task performance. More significant bits (e.g., MSBs) that have greater impact on task accuracy are refreshed more frequently, while less significant bits (e.g., LSBs) are refreshed less frequently. This localized differentiation optimizes the balance between data retention reliability and power consumption by tailoring refresh operations to the specific importance of each bit position rather than applying a uniform approach to all bits.
Solution Approach 2:
The patent segments the refresh operation by dividing the B-bit word into multiple bit positions and assigning different refresh intervals to each segment. The memory device is configured with separate refresh control for each bit position, allowing independent optimization of refresh timing based on the relative importance of each bit to the machine learning or signal processing task performance.
2Reliability
If refresh operations are performed frequently for all bits, then data loss is prevented, but system performance and productivity deteriorate
Solution Approach 1:
The patent applies local quality by assigning different refresh intervals to different bit positions based on their relative importance to task performance. More significant bits (e.g., MSBs) that have greater impact on task accuracy are refreshed more frequently, while less significant bits (e.g., LSBs) are refreshed less frequently. This localized differentiation optimizes the balance between data retention reliability and power consumption by tailoring refresh operations to the specific importance of each bit position rather than applying a uniform approach to all bits.
Solution Approach 2:
The patent implements dynamic refresh interval adjustment based on the specific machine learning or signal processing task being executed. The system dynamically determines the relative importance of each bit position for the current task and adjusts refresh intervals accordingly, rather than using fixed uniform intervals. This dynamic adaptation allows the system to optimize performance for different computational workloads.
3Adaptability or versatility
If bit positions are stored in separate sub-arrays, then differentiated refresh control is enabled, but device complexity increases
Solution Approach 1:
The patent segments the memory device into multiple sub-arrays, with each sub-array dedicated to storing a specific bit position of the B-bit word. This segmentation enables independent refresh control for each bit position, allowing the system to apply differentiated refresh intervals based on the relative importance of each bit to task performance. The segmented structure provides the adaptability needed for optimized refresh operations.
Solution Approach 2:
The patent achieves multi-functionality by designing the memory device to simultaneously support both traditional uniform refresh operations and the new differentiated refresh operations. The memory controller can selectively apply uniform refresh intervals across all sub-arrays or apply differentiated intervals based on task requirements, providing versatility in refresh control strategies.
Data Source
AI summary
A system and method for refreshing memory cells of a memory device includes storing each bit of a B-bit word in a different sub-array of a memory device. Each of the bits is associated with a bit position, and the memory device includes a plurality of sub-arrays. The system and method also include determining a refresh interval for a plurality of the bit positions based upon a relative importance of the plurality of the bit positions to a performance of a machine learning or signal processing task involving the B-bit word. The refresh interval is based upon a fidelity metric and a resource metric. The system and method further include refreshing the plurality of sub-arrays based upon the refresh interval determined for the plurality of bit positions, and dynamically updating the refresh interval for the plurality of bit positions upon receiving a new fidelity metric or a new resource metric.