AI-ENHANCED MEMORY SYSTEMS

Abstract

ABSTRACT AI-Enhanced Memory Systems The present disclosure introduces an AI-enhanced memory system 100 that optimizes data storage, retrieval, and resource allocation using machine learning. It comprises of memory hardware 102 that forms foundational storage layer, governed by AI-driven memory management software 104 which dynamically allocates resources. Machine learning models 106 predict memory requirements, supporting the predictive data access and caching module 110 that pre-loads frequently accessed data. The energy management module 112 adjusts power consumption, while the real-time health monitoring and predictive maintenance module 114 proactively detects potential memory issues. The system's self-healing system 134 autonomously corrects errors and reallocates resources, enhancing reliability. For adaptable performance, the scalability and adaptability framework 116 adjusts memory strategies, while granular memory throttling module 136 provides fine-grained control over memory bandwidth. The system also incorporates context-aware memory allocation module 124 and cross-platform memory coordination module 132. Reference Fig 1

Specification

Description:DETAILED DESCRIPTION

[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of AI-enhanced memory system and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00028] Referring to Fig. 1, AI-enhanced memory system 100 is disclosed in accordance with one embodiment of the present invention. It comprises of memory hardware 102, AI-driven memory management software 104, machine learning models 106, memory management unit (mmu) 108, predictive data access and caching module 110, energy management module 112, real-time health monitoring and predictive maintenance module 114, scalability and adaptability framework 116, security and access control module 118, data deduplication and compression module 120, thermal management module 122, context-aware memory allocation module 124, hybrid memory management interface 126, error detection, fault tolerance, and redundancy module 128, virtual memory swapping optimization module 130, cross-platform memory coordination module 132, self-healing system 134 and granular memory throttling module 136.

[00029] Referring to Fig. 1, the present disclosure provides details of an AI-enhanced memory system 100 designed to optimize data storage, retrieval, and resource allocation dynamically. This system leverages machine learning models 106 and AI-driven memory management software 104 to predict memory needs, allocate resources in real-time, and improve data access efficiency. In one embodiment, the AI-enhanced memory system may include key components such as memory hardware 102, memory management unit 108, and predictive data access and caching module 110, facilitating low-latency data retrieval and efficient memory use. The system also incorporates energy management module 112 and thermal management module 122 to reduce power consumption and control hardware temperature. Additional components such as self-healing system 134 and error detection, fault tolerance, and redundancy module 128 enhance system reliability and longevity.

[00030] Referring to Fig. 1, AI-enhanced memory system 100 is provided with memory hardware 102, which forms the physical foundation of the system, comprising DRAM, SSDs, and cache memory. This component enables data storage and retrieval and works under the control of AI-driven memory management software 104 to ensure optimal use of available memory resources. The memory hardware 102 interacts seamlessly with predictive data access and caching module 110 to pre-load frequently accessed data, reducing latency. Additionally, the energy management module 112 monitors memory hardware 102 to manage power consumption dynamically, powering down inactive memory blocks as needed.

[00031] Referring to Fig. 1, AI-enhanced memory system 100 is provided with AI-driven memory management software 104, which is responsible for managing memory resources in real-time. This software layer employs machine learning models 106 to analyze usage patterns and predict memory demands, dynamically allocating and deallocating memory to optimize performance. It works closely with memory management unit 108, which executes memory-related tasks based on decisions made by the software. Additionally, AI-driven memory management software 104 interacts with the security and access control module 118 to manage secure memory access, ensuring efficient and protected data handling.

[00032] Referring to Fig. 1, AI-enhanced memory system 100 is provided with machine learning models 106, which are the core of the AI's predictive capabilities. These models analyze historical data to anticipate future memory requirements and optimize data caching and memory allocation. Working in conjunction with predictive data access and caching module 110, the machine learning models 106 help minimize memory access latency. They also interact with self-healing system 134, refining memory management strategies over time by learning from system performance data and adjusting resource allocations accordingly.

[00033] Referring to Fig. 1, AI-enhanced memory system 100 is provided with memory management unit 108, which functions as an interface between AI-driven memory management software 104 and memory hardware 102. This unit executes commands from the software, handling memory allocation, deallocation, and access scheduling in real time. The memory management unit 108 plays a critical role in ensuring that predictive data access and caching module 110 and energy management module 112 operate efficiently by managing data flow and maintaining memory balance.

[00034] Referring to Fig. 1, AI-enhanced memory system 100 is provided with predictive data access and caching module 110, which anticipates data access needs based on historical trends and real-time processing demands. This module pre-loads frequently accessed data into high-speed memory segments, reducing retrieval time and enhancing system performance. It collaborates closely with machine learning models 106 to ensure that data caching decisions align with predicted memory needs, thereby reducing latency. Additionally, predictive data access and caching module 110 interfaces with memory hardware 102 to manage data storage locations effectively.
[00035] Referring to Fig. 1, AI-enhanced memory system 100 is provided with energy management module 112, which is designed to optimize the power usage of memory resources by dynamically controlling the power state of memory blocks. This module reduces energy consumption by identifying low-demand periods and powering down idle memory blocks. It interacts with AI-driven memory management software 104 to adjust energy settings in real-time based on predicted workload demands, ensuring memory hardware 102 remains efficient and responsive.

[00036] Referring to Fig. 1, AI-enhanced memory system 100 is provided with real-time health monitoring and predictive maintenance module 114, which continuously scans memory hardware 102 for signs of wear or degradation. This module detects and logs potential issues, allowing for proactive maintenance and reducing unexpected downtime. It works with self-healing system 134 to implement corrective actions automatically if errors are detected, contributing to the system's reliability and longevity.

[00037] Referring to Fig. 1, AI-enhanced memory system 100 is provided with scalability and adaptability framework 116, which allows the system to adjust its memory management strategies according to the workload and environment. This framework ensures that the system can scale from personal devices to data centers, modifying memory allocation approaches as required. It works with cross-platform memory coordination module 132 to synchronize memory resources across distributed systems, allowing seamless scaling across multiple computing environments.

[00038] Referring to Fig. 1, AI-enhanced memory system 100 is provided with security and access control module 118, which manages dynamic access rights and secures memory resources. This module enforces access protocols to protect sensitive data and prevent unauthorized access. It operates alongside AI-driven memory management software 104 to assign memory access based on user roles and application priority, ensuring both efficiency and security in memory handling.

[00039] Referring to Fig. 1, AI-enhanced memory system 100 is provided with data deduplication and compression module 120, which optimizes memory usage by identifying and eliminating redundant data in real time. This module compresses data that is infrequently accessed, freeing up memory space without affecting system performance. It collaborates with predictive data access and caching module 110 to manage data efficiently, particularly in high-demand environments, increasing the effective storage capacity of memory hardware 102.

[00040] Referring to Fig. 1, AI-enhanced memory system 100 is provided with thermal management module 122, which monitors and controls the temperature of memory components to prevent overheating. This module redistributes memory loads, throttles processes, and, if needed, activates cooling measures to protect memory hardware 102 from thermal stress. It works with energy management module 112 to balance performance with energy efficiency, ensuring reliable operation under varying thermal conditions.

[00041] Referring to Fig. 1, AI-enhanced memory system 100 is provided with context-aware memory allocation module 124, which adjusts memory allocation based on contextual data such as user, application type, and system load. This module prioritizes critical applications by assigning them higher memory bandwidth, ensuring that essential tasks perform optimally. It integrates with AI-driven memory management software 104 to adjust allocations dynamically, aligning memory resources with real-time operational needs.

[00042] Referring to Fig. 1, AI-enhanced memory system 100 is provided with hybrid memory management interface 126, which optimizes the use of different memory types such as DRAM, SSD, and NVM based on task requirements. This interface dynamically selects the most appropriate memory type for specific tasks, reducing wear on memory hardware 102 and extending its lifespan. It coordinates with memory management unit 108 to ensure efficient use of each memory technology.

[00043] Referring to Fig. 1, AI-enhanced memory system 100 is provided with error detection, fault tolerance, and redundancy module 128, which ensures data integrity by managing redundancy and fault tolerance. This module mirrors critical data across memory banks and dynamically reallocates memory in case of faults. It interfaces with self-healing system 134 to maintain continuous operation, redistributing memory loads as necessary to prevent interruptions in performance.

[00044] Referring to Fig. 1, AI-enhanced memory system 100 is provided with virtual memory swapping optimization module 130, which optimizes memory page swapping between physical and virtual memory. This module predicts high-demand periods and preloads frequently accessed pages, reducing page faults and maximizing memory efficiency. It works closely with AI-driven memory management software 104 to manage memory space allocation effectively during periods of peak demand.

[00045] Referring to Fig. 1, AI-enhanced memory system 100 is provided with cross-platform memory coordination module 132, which synchronizes memory resources across distributed environments like cloud systems, edge devices, and on-premises servers. This module allows the AI-enhanced memory system to operate efficiently across diverse platforms, collaborating with scalability and adaptability framework 116 to ensure consistent performance and resource sharing across interconnected systems.

[00046] Referring to Fig. 1, AI-enhanced memory system 100 is provided with self-healing system 134, which detects, diagnoses, and resolves memory errors automatically, ensuring uninterrupted system operation. This module isolates faulty memory blocks and reallocates resources to maintain performance, working alongside real-time health monitoring and predictive maintenance module 114 to extend the life of memory hardware 102 and reduce manual intervention requirements.

[00047] Referring to Fig. 1, AI-enhanced memory system 100 is provided with granular memory throttling module 136, which provides fine control over memory bandwidth for specific applications or processes. This module dynamically adjusts memory access rates to balance performance with energy efficiency. It interacts with context-aware memory allocation module 124 to prioritize resource-intensive tasks, ensuring critical applications receive adequate memory bandwidth while optimizing energy usage.

[00048] Referring to Fig 2, there is illustrated method 200 for AI-enhanced memory system 100. The method comprises:
At step 202, method 200 includes AI-driven memory management software 104 analyzing data usage patterns and predicting memory demands based on current and historical data;
At step 204, method 200 includes memory management unit 108 dynamically allocating memory resources according to the decisions made by AI-driven memory management software 104 to optimize performance;
At step 206, method 200 includes predictive data access and caching module 110 pre-loading frequently accessed data into high-speed memory segments to reduce retrieval time and latency;
At step 208, method 200 includes energy management module 112 detecting periods of low memory demand and powering down idle memory blocks to reduce energy consumption;
At step 210, method 200 includes real-time health monitoring and predictive maintenance module 114 continuously scanning memory hardware 102 for signs of wear or degradation and alerting the self-healing system 134 if errors are detected;
At step 212, method 200 includes self-healing system 134 isolating faulty memory blocks and reallocating resources to maintain system performance and prevent downtime;
At step 214, method 200 includes context-aware memory allocation module 124 adjusting memory allocation based on user priority, application type, or system load, ensuring that critical applications receive higher memory bandwidth;
At step 216, method 200 includes thermal management module 122 monitoring the temperature of memory hardware 102 and redistributing memory loads or activating cooling measures if overheating is detected;
At step 218, method 200 includes data deduplication and compression module 120 identifying redundant data in memory and compressing less-accessed data in real-time to maximize memory efficiency;
At step 220, method 200 includes cross-platform memory coordination module 132 synchronizing memory resources across distributed environments to ensure seamless resource sharing and optimized performance across interconnected systems.
[00049] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00050] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00051] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. An AI-enhanced memory system 100 comprising of
memory hardware 102 to provide the physical foundation for data storage and retrieval;
AI-driven memory management software 104 to analyze data patterns and manage memory allocation dynamically;
machine learning models 106 to predict memory requirements and optimize resource allocation;
memory management unit 108 to execute memory-related tasks based on AI decisions;
predictive data access and caching module 110 to reduce latency by pre-loading frequently accessed data;
energy management module 112 to reduce power consumption by managing memory block activity;
real-time health monitoring and predictive maintenance module 114 to detect wear and prevent memory hardware failure;
scalability and adaptability framework 116 to enable seamless scaling across diverse computing environments;
security and access control module 118 to manage dynamic access rights and ensure data security;
data deduplication and compression module 120 to optimize memory usage by removing redundant data;
thermal management module 122 to control memory hardware temperature and prevent overheating;
context-aware memory allocation module 124 to adjust allocation based on application priority and user context;
hybrid memory management interface 126 to optimize use of different memory types like DRAM and SSD;
error detection, fault tolerance, and redundancy module 128 to ensure data integrity through fault tolerance;
virtual memory swapping optimization module 130 to manage efficient memory swapping between physical and virtual memory;
cross-platform memory coordination module 132 to synchronize resources across distributed systems;
self-healing system 134 to automatically isolate and address memory errors; and
granular memory throttling module 136 to control memory bandwidth for resource optimization.
2. The AI-enhanced memory system 100 as claimed in claim 1, wherein memory hardware 102 is configured to support adaptive storage and retrieval by integrating with AI-driven memory management software 104, ensuring dynamic allocation based on real-time usage patterns for improved efficiency and reduced latency.

3. The AI-enhanced memory system 100 as claimed in claim 1, wherein AI-driven memory management software 104 is configured to analyze data access patterns, predict future memory requirements, and dynamically allocate resources in response to system demands, optimizing memory utilization and preventing overprovisioning.

4. The AI-enhanced memory system 100 as claimed in claim 1, wherein machine learning models 106 are configured to leverage historical data to preemptively allocate memory, enhance data caching, and continuously refine resource distribution through self-learning, adapting to evolving usage trends.

5. The AI-enhanced memory system 100 as claimed in claim 1, wherein predictive data access and caching module 110 is configured to reduce memory access latency by pre-loading frequently accessed data into high-speed memory, enabling faster data retrieval for high-demand applications.

6. The AI-enhanced memory system 100 as claimed in claim 1, wherein energy management module 112 is configured to monitor memory usage, deactivate idle memory blocks during low-demand periods, and scale energy resources dynamically based on workload predictions, reducing overall power consumption.

7. The AI-enhanced memory system 100 as claimed in claim 1, wherein real-time health monitoring and predictive maintenance module 114 is configured to continuously monitor memory hardware 102 for degradation, proactively detect potential failures, and enable self-healing system 134 to isolate faults and reassign resources, ensuring system reliability.

8. The AI-enhanced memory system 100 as claimed in claim 1, wherein self-healing system 134 is configured to autonomously diagnose, isolate, and correct memory errors, reallocating resources to unaffected sectors and maintaining uninterrupted performance without manual intervention.

9. The AI-enhanced memory system 100 as claimed in claim 1, wherein granular memory throttling module 136 is configured to provide fine-grained control over memory bandwidth, adjusting access rates for critical applications, balancing performance and energy efficiency in resource-constrained environments.

10. The AI-enhanced memory system 100 as claimed in claim 1, wherein method comprises of
AI-driven memory management software 104 analyzing data usage patterns and predicting memory demands based on current and historical data;
memory management unit 108 dynamically allocating memory resources according to the decisions made by AI-driven memory management software 104 to optimize performance;
predictive data access and caching module 110 pre-loading frequently accessed data into high-speed memory segments to reduce retrieval time and latency;
energy management module 112 detecting periods of low memory demand and powering down idle memory blocks to reduce energy consumption;
real-time health monitoring and predictive maintenance module 114 continuously scanning memory hardware 102 for signs of wear or degradation and alerting the self-healing system 134 if errors are detected;
self-healing system 134 isolating faulty memory blocks and reallocating resources to maintain system performance and prevent downtime;
context-aware memory allocation module 124 adjusting memory allocation based on user priority, application type, or system load, ensuring that critical applications receive higher memory bandwidth;
thermal management module 122 monitoring the temperature of memory hardware 102 and redistributing memory loads or activating cooling measures if overheating is detected;
data deduplication and compression module 120 identifying redundant data in memory and compressing less-accessed data in real-time to maximize memory efficiency; and
cross-platform memory coordination module 132 synchronizing memory resources across distributed environments to ensure seamless resource sharing and optimized performance across interconnected systems.

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