Storage Device Memory Region Wear Balancing
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Solution Overview
Problem
Storage devices with memory regions of different bit-densities face challenges in evenly distributing data workload, leading to uneven wear and reduced lifespan, particularly in regions with higher bit-density.
Innovation Solution
A storage device with a controller that uses reinforcement learning to dynamically adjust the sizes of memory regions based on data distribution patterns, wear level information, and reinforcement learning results, ensuring more even wear and extending the lifespan by optimizing data distribution across regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is distributed to memory regions with different bit-densities, then storage capacity is optimized, but wear becomes uneven and lifespan is reduced
Solution Approach 1:
The patent implements dynamic adjustment of memory region sizes based on real-time wear levels and usage patterns. The controller continuously monitors wear information from different bit-density regions and reallocates data distribution accordingly, making the system adaptive rather than static. This resolves the contradiction by allowing the system to optimize storage capacity while simultaneously preventing excessive wear on any single region through continuous dynamic adjustment.
Solution Approach 2:
The patent changes the parameters of data distribution by adjusting the weight or priority assigned to different memory regions based on their bit-density characteristics and current wear states. The controller modifies distribution parameters dynamically, allocating more data to regions with lower wear and fewer bit-density operations, while reducing allocation to heavily worn regions. This parameter adjustment resolves the contradiction between maximizing storage utilization and maintaining uniform wear across regions.
2Productivity
If higher bit-density regions are used, then storage efficiency increases, but wear rate increases and lifespan decreases
Solution Approach 1:
The patent applies different data distribution strategies to different memory regions based on their local characteristics. High bit-density regions receive different allocation weights compared to low bit-density regions, with the controller adjusting local distribution parameters according to each region's wear level and performance characteristics. This local quality approach allows the system to maintain high storage efficiency in fast regions while protecting slower, more wear-prone regions, thus resolving the contradiction between productivity and lifespan.
Solution Approach 2:
The patent implements a feedback mechanism where the controller continuously monitors wear information from memory regions and uses this feedback to adjust data distribution patterns. When high bit-density regions show increased wear, the system receives feedback and automatically reduces data allocation to those regions, redirecting writes to less worn regions. This closed-loop feedback system resolves the contradiction by dynamically balancing storage efficiency with wear management based on real-time conditions.
Data Source
AI summary
A storage device includes a memory device including a first memory region, a second memory region, and a third memory region, the first memory region having a lowest bit-density relative to the second memory region and the third memory region, a second memory region having a medium bit-density relative to the first memory region and the third memory region, and a third memory region having a highest bit-density relative to the first memory region and the second memory region; and a controller configured to control the memory device The controller is configured to distribute data received from a host to the first to third memory regions based on attributes of the data, to determine a current state based on a data distribution amount for each of the first to third memory regions and a respective size of each of the first to third memory regions, and to perform an action of increasing or decreasing a size of the second memory region under the current state based on a reinforcement learning result for mitigating a reduction in lifespan of the third memory region.