Dynamic Read Write Buffer Allocation in Memory Sub-Systems
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Solution Overview
Problem
Memory sub-systems face performance degradation due to insufficient buffer capacity for read and write operations, as static allocation fails to adapt to changing workload ratios between read and write operations, leading to suboptimal performance across varying application contexts.
Innovation Solution
A logic circuit in the memory sub-system tracks operation statistics to predict an optimized buffer split using a machine learning model, such as an artificial neural network, which adjusts the read and write buffer allocations dynamically based on application context, ensuring optimal performance by generating a lookup table for buffer memory split ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static buffer allocation is used, then device complexity is reduced, but productivity deteriorates due to inability to adapt to changing workload ratios
Solution Approach 1:
The patent implements dynamic buffer allocation where the read buffer and write buffer sizes are adjusted in real-time based on the detected workload ratio. The buffer manager continuously monitors read and write operations and reallocates buffer memory accordingly, transforming the static buffer allocation into a dynamic system that adapts to changing workload conditions, thereby resolving the contradiction between maintaining simple device architecture and achieving high productivity.
Solution Approach 2:
The patent employs a feedback mechanism where the buffer manager detects the ratio of read to write operations and uses this information to adjust buffer allocations. This closed-loop control system continuously monitors workload patterns and provides feedback to optimize buffer distribution, enabling the system to maintain high performance without requiring complex manual configuration or prediction algorithms.
2Productivity
If buffer allocation is optimized for specific workload ratios, then productivity improves for those workloads, but adaptability deteriorates when workload ratios change
Solution Approach 1:
The system dynamically adjusts buffer allocations based on the current workload ratio detected from ongoing read and write operations. Rather than being optimized for a fixed workload pattern, the buffer manager continuously adapts the read and write buffer sizes to match the actual operational demands, thereby maintaining high productivity across varying workload conditions without sacrificing adaptability.
3Adaptability or versatility
If machine learning models are used to predict buffer split ratios, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent uses a feedback-based approach where the buffer manager directly monitors the ratio of read to write operations and adjusts buffer allocations accordingly. This simple feedback mechanism achieves adaptability without requiring complex machine learning models, prediction algorithms, or extensive training data, thereby resolving the contradiction between achieving high adaptability and maintaining manageable system complexity.
Data Source
AI summary
A memory sub-system operable to dynamically adjusts its allocation of a read buffer and a write buffer. For example, after the read buffer and the write buffer are allocated to have a first ratio between their capacities, data communicated for read commands is buffered in the read buffer; and data communicated for write commands is buffered in the write buffer. Statistics of read commands and write commands received in the memory sub-system during a first time period can be tracked to determine a current application context of operating the memory sub-system. A second ratio is determined via a predictive model from the current application context. The allocation of the read buffer and the write buffer is adjusted according to the second ratio for operating the memory sub-system during a second time period following the first time period.