Memory Upgrade Path Optimization via Segmented Array Analysis
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Solution Overview
Problem
Current computer system configuration management lacks a systematic approach to optimize memory upgrades, balancing speed, capacity, and cost effectively, which is essential for overall system efficiency.
Innovation Solution
An automated mechanism that analyzes memory configuration criteria such as capacity, speed, and cost to recommend optimized reconfiguration paths, utilizing a system with modules for requirements analysis and reconfiguration, including a requirements module, an analysis module, and a reconfiguration module to determine potential changes and generate recommendations for improving capacity and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If memory device count is increased to expand capacity, then capacity is improved, but array speed deteriorates
Solution Approach 1:
The patent segments the memory array into multiple independent memory devices, each capable of operating at high speed. By organizing memory into separate devices rather than using a single large device, the system achieves both high capacity and high speed through parallel operation of multiple segmented units.
Solution Approach 2:
The patent transitions from a single-dimension approach (increasing device count) to a multi-dimensional approach by considering capacity, speed, and cost as separate optimization dimensions. The systematic method evaluates tradeoffs across multiple dimensions simultaneously, allowing optimization in one dimension without completely sacrificing performance in others.
2Speed
If faster memory devices are used to improve speed, then array speed is improved, but cost increases
Solution Approach 1:
The patent systematically varies key parameters including memory device count, individual device speed, and device capacity to find optimal configurations. By changing these parameters in a structured manner, the method identifies configurations that achieve desired performance at minimized cost, rather than simply selecting the fastest or cheapest option.
Solution Approach 2:
The patent applies partial action by selecting memory device speeds that are sufficient but not excessive for the given requirements. Rather than uniformly using the fastest available devices, the systematic approach identifies the minimum speed threshold needed and selects devices that meet this threshold at optimal cost.
3Quantity of substance
If more memory devices are added to increase capacity, then capacity is improved, but device complexity increases
Solution Approach 1:
The patent employs universal configuration patterns that can be applied across different memory device counts and types. The systematic method uses standardized evaluation criteria and optimization algorithms that work regardless of the specific number of devices, reducing the complexity of managing diverse configurations through universal principles.
Solution Approach 2:
The systematic optimization method performs automatic evaluation and selection of configurations, reducing the need for manual analysis of complex multi-device arrangements. The algorithm self-manages the complexity by systematically evaluating tradeoffs and selecting optimal configurations without requiring deep manual intervention in the complex configuration space.
4Quantity of substance
If memory configuration is optimized for a single criterion, then that criterion is improved, but overall system efficiency deteriorates
Solution Approach 1:
The patent systematically varies multiple parameters including capacity, speed, and cost simultaneously rather than optimizing a single parameter in isolation. By changing these parameters in a coordinated manner and evaluating their combined effects, the method achieves balanced optimization that improves overall system efficiency while meeting capacity requirements.
Solution Approach 2:
The patent moves from single-criterion optimization to multi-criterion optimization by adding dimensions for capacity, speed, and cost evaluation. This multi-dimensional approach allows the system to find configurations that balance all three criteria, preventing degradation of overall efficiency that would result from single-criterion optimization.
Data Source
AI summary
An apparatus, system, and method are disclosed for memory upgrade optimization. A requirements module 402 receives one or more of a capacity upgrade goal 1306 for an overall capacity of the array 706 and a performance upgrade goal 1308 for an overall performance of the array 706. An analysis module 404 identifies a first potential capacity change 1310 that can be achieved at a lower overall performance and a second potential capacity change 1314 that can be achieved at a higher overall performance. A reconfiguration module 406 generates one or more of a first reconfiguration recommendation 1312 calculated to yield an overall capacity improvement that takes into consideration the capacity upgrade goal 1306 and the first potential capacity change 1310 and a second reconfiguration recommendation 1316 calculated to yield an overall performance improvement that takes into consideration the performance upgrade goal 1308 and the second potential capacity change 1314.