Prediction-Based Garbage Collection for Memory Controllers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing garbage collection methods in memory devices rely on static idle time durations and garbage collection windows, which do not adapt to varying device usage patterns, leading to inefficient garbage collection events and potential system performance issues.
Innovation Solution
Implementing a controller-based machine learning approach using an artificial neural network (ANN) to predict ideal garbage collection triggers and durations based on specific usage patterns, allowing for dynamic timing profiles that adjust garbage collection events according to actual system needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static idle time durations and garbage collection windows are used, then the system maintains simple control logic, but garbage collection efficiency deteriorates due to inability to adapt to varying usage patterns
Solution Approach 1:
The patent implements dynamic timing profiles that adjust garbage collection parameters based on real-time usage patterns. The system transitions from static, fixed timing to dynamic, adaptive timing where collection windows and idle durations are modified according to observed device behavior, directly resolving the contradiction between efficiency and complexity
Solution Approach 2:
The system incorporates feedback mechanisms that monitor usage patterns and adjust garbage collection timing accordingly. By continuously observing device behavior and using this information to modify collection parameters, the system achieves adaptive efficiency without requiring overly complex predetermined control logic
2Productivity
If longer garbage collection windows are allocated, then more garbage collection can be performed, but system performance deteriorates due to extended interruption of operations
Solution Approach 1:
The patent implements periodic garbage collection events spaced at optimized intervals rather than continuous or excessively long single windows. By dividing collection into periodic bursts separated by normal operation intervals, the system achieves substantial garbage collection throughput while maintaining system responsiveness between collection events
Solution Approach 2:
The system dynamically adjusts garbage collection window lengths based on usage patterns and system state. Rather than allocating uniformly long windows that always interrupt operations, the collection duration is adapted in real-time to balance throughput requirements with system performance, performing longer collections when usage patterns indicate appropriate intervals
Data Source
AI summary
Apparatuses and methods can be related to performing prediction based garbage collection. Performing prediction based garbage collection can include performing a first instance of garbage collection, of the memory device, using a first circuitry of the controller and generating a prediction using a second circuitry of the controller. A confidence interval can also be generated for the prediction using the second circuitry of the controller. Responsive to determining that the confidence interval is greater than a threshold, a second instance of garbage collection, of the memory device, can be triggered using the first circuitry of the controller where the first instance of garbage collection is triggered before the second instance of garbage collection.