INTEGRATED SYSTEM ON CHIP FOR ENHANCED AI FUNCTIONALITY

Abstract

ABSTRACT INTEGRATED SYSTEM ON CHIP FOR ENHANCED AI FUNCTIONALITY The present disclosure introduces an integrated system on chip for enhanced AI functionality 100 designed to optimize AI processing with components such as central processing unit 102, graphics processing unit 104, and AI accelerator unit 106. This architecture includes a memory subsystem 108 for high-speed data access and an interconnect fabric 110 that enables seamless communication across modules. The system features a dynamic resource allocation engine 112 to distribute processing loads efficiently and an adaptive power management module 114 that minimizes energy consumption through real-time adjustments. Real-time data processing unit 116 handles streaming data with low latency, essential for immediate responses. The security module with AI-powered threat detection 118 safeguards data integrity, while a multi-level cache system 120 reduces access delays. Additional components include cross-component data fusion engine 122, programmable AI co-processing units 124, thermal management system 126, and connectivity protocols module 134 for comprehensive integration. Reference Fig 1

Specification

Description:INTEGRATED SYSTEM ON CHIP FOR ENHANCED AI FUNCTIONALITY
TECHNICAL FIELD
[0001] The present innovation relates to integrated circuit design, specifically a System on Chip (SoC) architecture optimized to enhance AI functionality through advanced processing, power management, and scalability features.

BACKGROUND

[0002] Artificial Intelligence (AI) has become integral in various sectors, from healthcare and finance to autonomous systems and IoT devices. However, the efficient implementation of AI in compact, energy-efficient hardware remains a significant challenge. Traditional systems typically rely on discrete processors like CPUs and GPUs to perform high-computation tasks. While powerful, these setups lack scalability, consume significant power, and often struggle to handle complex AI workloads such as deep learning models. Users have turned to specialized AI accelerators, such as Tensor Processing Units (TPUs) and Field-Programmable Gate Arrays (FPGAs), which improve performance but introduce design complexities, high costs, and limited adaptability in certain applications.

[0003] This invention, an Integrated System on Chip (SoC) designed specifically for AI, overcomes these drawbacks by consolidating multiple AI-optimized processing units-CPU, GPU, and dedicated AI accelerators-onto a single chip. Unlike conventional SoCs that focus on general-purpose functionality, this invention is tailored to efficiently manage AI workloads, offering adaptive processing and resource allocation features that dynamically distribute tasks across various units based on workload demand. This not only enhances power efficiency through techniques like Dynamic Voltage and Frequency Scaling (DVFS) but also optimizes real-time processing, which is crucial for applications such as autonomous vehicles and industrial automation.


[0004] The novelty of this SoC lies in its AI-centric architecture, high-bandwidth memory subsystem, and integrated interconnect fabric, which collectively reduce latency and improve data throughput. Additionally, features like an adaptive learning mechanism allow the system to optimize performance based on usage patterns, while built-in security modules enhance data protection. This invention provides an integrated, scalable solution that meets the demands of AI applications across diverse fields, making it a cost-effective, powerful, and energy-efficient alternative to existing options.

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to enhance AI functionality by providing an Integrated System on Chip (SoC) specifically designed for optimized performance, scalability, and energy efficiency in AI applications.

[0006] Another object of the invention is to improve power management through dynamic voltage and frequency scaling (DVFS), which minimizes energy consumption while maintaining optimal processing speeds.

[0007] Another object of the invention is to enable real-time data processing capabilities, essential for applications in autonomous systems and industrial automation that require immediate decision-making and responses.

[0008] Another object of the invention is to reduce latency and improve data throughput by incorporating a high-bandwidth memory subsystem and efficient interconnect fabric for seamless data transfer between components.
[0009] Another object of the invention is to support flexible integration with various devices and sensors, enhancing its applicability in edge computing, IoT, and mobile computing environments.

[00010] Another object of the invention is to increase data security through dedicated hardware-based security modules, protecting sensitive information processed within the SoC.

[00011] nother object of the invention is to offer a scalable architecture that allows multiple SoCs to be interconnected, enabling distributed processing of complex AI tasks across multiple devices.

[00012] Another object of the invention is to enhance adaptability by incorporating an AI-driven adaptive learning mechanism, allowing the SoC to optimize performance based on usage patterns over time.

[00013] Another object of the invention is to provide a compact, cost-effective solution that consolidates AI-optimized CPUs, GPUs, and accelerators, reducing the physical footprint and manufacturing costs of high-performance AI hardware.

[00014] Another object of the invention is to enable efficient edge processing, reducing reliance on cloud resources and lowering latency for applications requiring high-fidelity AI inference directly on devices.

SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, integrated system on chip for enhanced AI functionality is presented. The invention relates to an Integrated System on Chip (SoC) architecture specifically designed to enhance AI functionality. By consolidating CPU, GPU, and dedicated AI accelerators onto a single chip, the SoC optimizes performance, energy efficiency, and scalability for AI workloads. Key features include adaptive resource allocation, real-time processing, and advanced power management. The invention also integrates high-bandwidth memory and security modules, making it ideal for applications in edge computing, IoT, and autonomous systems. This SoC provides a compact, powerful, and cost-effective solution for modern AI applications.

[00016] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00017] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00018] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00019] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00020] FIG. 1 is component wise drawing for integrated system on chip for enhanced AI functionality.

[00021] FIG 2 is working methodology of integrated system on chip for enhanced AI functionality.

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 integrated system on chip for enhanced AI functionality 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, integrated system on chip for enhanced AI functionality 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of central processing unit (CPU) module 102, graphics processing unit (GPU) module 104, AI accelerator unit 106, memory subsystem 108, interconnect fabric 110, dynamic resource allocation engine 112, adaptive power management module 114, real-time data processing unit 116, security module with AI-powered threat detection 118, multi-level cache system 120, cross-component data fusion engine 122, programmable AI co-processing units 124, thermal management system 126, flexible i/o interface module 128, edge processing unit 130, adaptive learning mechanism module 132, connectivity protocols module 134, on-chip model compression unit 136.

[00029] Referring to Fig. 1, the present disclosure provides details of integrated system on chip for enhanced AI functionality 100. It is an architecture designed to optimize AI performance, energy efficiency, and scalability by integrating multiple specialized processing units. The system on chip 100 may be provided with key components such as central processing unit (CPU) module 102, graphics processing unit (GPU) module 104, and AI accelerator unit 106, which collectively enhance AI-specific computations. In one embodiment, the architecture includes memory subsystem 108 and interconnect fabric 110 to enable high-speed data transfer and reduce latency. Additional components like adaptive power management module 114 and real-time data processing unit 116 further optimize performance in edge computing environments. Security module with AI-powered threat detection 118 and connectivity protocols module 134 provide enhanced data protection and seamless communication, making it suitable for diverse AI applications across industries.

[00030] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with central processing unit (CPU) module 102, which handles general-purpose tasks and overall system control. The CPU module 102 is optimized for coordination, interfacing with the AI accelerator unit 106 and graphics processing unit (GPU) module 104 to balance workload distribution across components. By managing data flow and execution commands, the CPU module 102 ensures seamless interworking and effective resource utilization, making it a central controller in the architecture.

[00031] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with graphics processing unit (GPU) module 104, which supports parallel processing, essential for handling deep learning and graphics-intensive tasks. The GPU module 104 works in tandem with the AI accelerator unit 106 to accelerate computation-heavy AI tasks, such as neural network training. This module enhances the system's ability to perform high-throughput calculations, reducing the load on the CPU module 102 and enabling faster processing of complex data.

[00032] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with AI accelerator unit 106, designed specifically for AI-related computations, utilizing specialized architectures like tensor processing for efficient handling of matrix operations. The AI accelerator unit 106 works closely with the memory subsystem 108 to access large datasets quickly, supporting rapid execution of AI algorithms. This targeted processing capability enables high-performance AI tasks, reducing latency and optimizing power use within the system.

[00033] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with memory subsystem 108, which includes high-bandwidth memory components to store and provide quick access to data for all processing modules. The memory subsystem 108 interfaces seamlessly with the interconnect fabric 110, allowing efficient data transfer between the CPU module 102, GPU module 104, and AI accelerator unit 106. This subsystem minimizes latency and ensures that each processing unit receives data promptly for uninterrupted AI operations.

[00034] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with interconnect fabric 110, a high-speed data pathway that facilitates communication between components such as the CPU module 102, GPU module 104, and memory subsystem 108. The interconnect fabric 110 reduces data bottlenecks, enabling smooth data flow essential for real-time processing. This fabric allows each component to access and share data effectively, supporting the system's overall performance and ensuring coordinated processing across modules.

[00035] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with dynamic resource allocation engine 112, which intelligently manages resource distribution between the CPU module 102, GPU module 104, and AI accelerator unit 106 based on real-time workload demands. The dynamic resource allocation engine 112 optimizes system performance by reallocating processing power as needed, preventing bottlenecks and ensuring energy-efficient operation. It continuously monitors task loads, adjusting resource availability to enhance processing efficiency across the system.


[00036] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with adaptive power management module 114, which minimizes power consumption by dynamically adjusting voltage and frequency for each component. The adaptive power management module 114 works in concert with the dynamic resource allocation engine 112 to monitor energy use and adjust power supply according to each module's activity levels. This module is essential for battery-powered devices and enables prolonged operation while maintaining high performance across various applications.

[00037] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with real-time data processing unit 116, which ensures rapid processing of streaming data, essential for applications like autonomous systems. The real-time data processing unit 116 utilizes low-latency data paths in collaboration with the memory subsystem 108 and interconnect fabric 110 to handle data without delays. This unit is crucial for applications that require immediate response, such as AI-driven safety systems, providing timely and accurate data processing.

[00038] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with security module with AI-powered threat detection 118, which safeguards data and computations from potential security breaches. The security module 118 includes encryption and decryption capabilities, ensuring secure data handling across components like the CPU module 102 and memory subsystem 108. By utilizing AI algorithms for real-time threat detection, this module proactively identifies and responds to security risks, enhancing the overall safety and integrity of the system.

[00039] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with multi-level cache system 120, which reduces data access latency and improves throughput by storing frequently used data closer to the processing units. The multi-level cache system 120 interacts closely with the memory subsystem 108 and each processing module, including the GPU module 104 and AI accelerator unit 106. This system efficiently manages data retrieval, minimizing processing delays and enabling smoother execution of AI workloads.

[00040] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with cross-component data fusion engine 122, which aggregates and processes data from multiple sources such as sensors and external devices. The cross-component data fusion engine 122 enhances data quality and decision-making by integrating information across the CPU module 102, GPU module 104, and memory subsystem 108. This engine supports multi-modal data processing, making the system versatile for applications like smart cities and autonomous vehicles.

[00041] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with programmable AI co-processing units 124, which allow developers to customize processing tasks for specific AI applications. The programmable AI co-processing units 124 enable flexible deployment of different AI models and work alongside the AI accelerator unit 106 to handle specialized computations. This adaptability makes the system suitable for a wide range of use cases, from healthcare to industrial automation.


[00042] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with thermal management system 126, which monitors and regulates the temperature of the SoC components, ensuring stable performance. The thermal management system 126 uses AI algorithms to predict thermal behaviour and adjust power levels in collaboration with the adaptive power management module 114. This proactive thermal regulation prevents overheating, enhancing component longevity and operational safety.

[00043] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with flexible i/o interface module 128, which supports various input/output interfaces such as SPI, I2C, UART, and USB, facilitating seamless communication with external devices. The flexible i/o interface module 128 allows the SoC to interact easily with sensors, IoT devices, and other peripherals, making it versatile for applications that require connectivity with diverse systems in environments like IoT and edge computing.

[00044] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with edge processing unit 130, which enables local data processing on devices at the edge of the network, reducing reliance on cloud resources. The edge processing unit 130 handles AI inference tasks directly on-device, interacting with the memory subsystem 108 to quickly access and process data. This capability is essential for applications that require low latency, such as real-time monitoring and autonomous operations.

[00045] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with adaptive learning mechanism module 132, which allows the system to optimize its performance based on historical usage patterns. The adaptive learning mechanism module 132 collaborates with the dynamic resource allocation engine 112 to refine resource distribution according to the specific needs of applications over time. This module enhances system efficiency by making intelligent adjustments, ensuring continuous improvement in handling AI tasks.

[00046] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with connectivity protocols module 134, which includes built-in support for wireless protocols such as Wi-Fi, Bluetooth, and Zigbee. The connectivity protocols module 134 enables the SoC to communicate seamlessly with other devices, making it suitable for IoT and smart device ecosystems. This module works in conjunction with the flexible i/o interface module 128 to provide comprehensive connectivity options.

[00047] Referring to Fig. 1, integrated system on chip for enhanced AI functionality 100 is provided with on-chip model compression unit 136, which reduces the memory footprint and computational requirements of AI models, facilitating efficient model deployment on resource-constrained devices. The on-chip model compression unit 136 optimizes models for execution within the AI accelerator unit 106, enabling the system to run complex AI applications without compromising performance. This unit is essential for applications that require high-performance AI within a compact and efficient design.

[00048] Referring to Fig 2, there is illustrated method 200 for integrated system on chip for enhanced AI functionality 100. The method comprises:
At step 202, method 200 includes initializing the cpu module 102, which manages system startup and allocates initial processing resources across the SoC;
At step 204, method 200 includes the dynamic resource allocation engine 112 monitoring incoming workloads and distributing processing tasks between the CPU module 102, GPU module 104, and AI accelerator unit 106 based on task requirements;
At step 206, method 200 includes the real-time data processing unit 116 beginning to handle streaming data, which it processes in collaboration with the memory subsystem 108 for low-latency data access essential to time-sensitive applications;
At step 208, method 200 includes activating the AI accelerator unit 106 to perform intensive AI computations, such as matrix operations and neural network processing, in sync with data stored and retrieved from the memory subsystem 108;
At step 210, method 200 includes the multi-level cache system 120 caching frequently accessed data, reducing access latency and enhancing throughput for ongoing processing tasks;
At step 212, method 200 includes executing data aggregation via the cross-component data fusion engine 122, which combines data from sensors, external devices, and internal modules to enable informed decision-making and AI model accuracy;
At step 214, method 200 includes adaptive energy management by the adaptive power management module 114, which adjusts voltage and frequency levels in real-time to optimize power consumption based on each module's activity;
At step 216, method 200 includes the thermal management system 126 monitoring the SoC's temperature, dynamically adjusting power settings in collaboration with the adaptive power management module 114 to prevent overheating and maintain performance;
At step 218, method 200 includes the security module with AI-powered threat detection 118 actively monitoring data interactions for security threats, ensuring data integrity throughout processing and storage within the SoC;
At step 220, method 200 includes transferring processed data via the interconnect fabric 110, which facilitates high-speed data communication across components such as the CPU module 102 and GPU module 104;
At step 222, method 200 includes compressing AI models within the on-chip model compression unit 136, optimizing model size and efficiency for execution on resource-constrained devices;
At step 224, method 200 includes enabling wireless data transmission through the connectivity protocols module 134, supporting integration with other devices and external networks essential for IoT and smart device applications;
At step 226, method 200 includes the adaptive learning mechanism module 132 continuously refining system performance based on usage patterns, allowing the SoC to improve efficiency over time and adapt to specific application demands.

[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 integrated system on chip for enhanced AI functionality 100 comprising of
central processing unit 102 to manage system control and allocate processing resources;
graphics processing unit 104 to handle parallel processing tasks for AI workloads;
AI accelerator unit 106 to perform specialized computations for deep learning models;
memory subsystem 108 to store and provide rapid access to data;
interconnect fabric 110 to enable high-speed communication between components;
dynamic resource allocation engine 112 to distribute tasks based on workload demands;
adaptive power management module 114 to optimize power usage dynamically;
real-time data processing unit 116 to process streaming data with low latency;
security module with AI-powered threat detection 118 to monitor and protect data integrity;
multi-level cache system 120 to reduce latency by caching frequently accessed data;
cross-component data fusion engine 122 to aggregate data from multiple sources;
programmable AI co-processing units 124 to customize tasks for specific AI applications;
thermal management system 126 to regulate temperature and prevent overheating;
flexible i/o interface module 128 to enable connectivity with various external devices;
edge processing unit 130 to perform AI inference locally on edge devices;
adaptive learning mechanism module 132 to improve performance based on usage patterns;
connectivity protocols module 134 to support wireless communication; and
on-chip model compression unit 136 to optimize model size for efficient execution

2. The integrated system on chip for enhanced AI functionality 100 as claimed in claim 1, wherein central processing unit 102 is configured to allocate processing resources dynamically across multiple modules, managing system control with minimal latency to optimize performance and resource utilization.

3. The integrated system on chip for enhanced AI functionality 100 as claimed in claim 1, wherein graphics processing unit 104 is configured to handle parallel processing tasks with high efficiency, supporting the execution of deep learning models and complex computations in collaboration with the AI accelerator unit.

4. The integrated system on chip for enhanced AI functionality 100 as claimed in claim 1, wherein AI accelerator unit 106 is configured to execute AI-specific computations, leveraging specialized architectures to accelerate neural network processing and matrix operations for optimized data throughput and model accuracy.

5. The integrated system on chip for enhanced AI functionality 100 as claimed in claim 1, wherein memory subsystem 108 is configured to provide high-bandwidth, low-latency access to data for all processing modules, enabling seamless interaction and rapid data retrieval to support real-time applications.

6. The integrated system on chip for enhanced AI functionality 100 as claimed in claim 1, wherein dynamic resource allocation engine 112 is configured to monitor workload distribution and dynamically reallocate processing resources among the CPU module 102, GPU module 104, and AI accelerator unit 106 based on current computational demands, ensuring efficient resource management.

7. The integrated system on chip for enhanced AI functionality 100 as claimed in claim 1, wherein adaptive power management module 114 is configured to optimize power consumption in real-time through voltage and frequency adjustments, extending operational lifespan and reducing energy costs in battery-powered devices.

8. The integrated system on chip for enhanced AI functionality 100 as claimed in claim 1, wherein real-time data processing unit 116 is configured to manage streaming data processing with minimal delay, enabling immediate response and action for applications requiring real-time analytics and decision-making.

9. The integrated system on chip for enhanced AI functionality 100 as claimed in claim 1, wherein security module with AI-powered threat detection 118 is configured to protect data integrity by monitoring for security risks and applying AI-driven threat detection algorithms, ensuring secure data handling within the system.

10. The integrated system on chip for enhanced AI functionality 100 as claimed in claim 1, wherein method comprises of
CPU module 102 initializing system startup and allocating initial processing resources across the SoC;

dynamic resource allocation engine 112 monitoring incoming workloads and distributing processing tasks between the CPU module 102, GPU module 104, and AI accelerator unit 106 based on task requirements;

real-time data processing unit 116 beginning to handle streaming data, processing it in collaboration with the memory subsystem 108 for low-latency data access essential to time-sensitive applications;

AI accelerator unit 106 performing intensive AI computations, such as matrix operations and neural network processing, in sync with data stored and retrieved from the memory subsystem 108;

multi-level cache system 120 caching frequently accessed data, reducing access latency and enhancing throughput for ongoing processing tasks;

cross-component data fusion engine 122 executing data aggregation, combining data from sensors, external devices, and internal modules to enable informed decision-making and AI model accuracy;

adaptive power management module 114 dynamically adjusting voltage and frequency levels in real-time to optimize power consumption based on each module's activity;

thermal management system 126 monitoring the SoC's temperature, dynamically adjusting power settings in collaboration with the adaptive power management module 114 to prevent overheating and maintain performance;

security module with AI-powered threat detection 118 actively monitoring data interactions for security threats, ensuring data integrity throughout processing and storage within the SoC;

interconnect fabric 110 transferring processed data, facilitating high-speed data communication across components such as the CPU module 102 and GPU module 104;

on-chip model compression unit 136 compressing AI models, optimizing model size and efficiency for execution on resource-constrained devices;

connectivity protocols module 134 enabling wireless data transmission, supporting integration with other devices and external networks essential for IoT and smart device applications;

adaptive learning mechanism module 132 continuously refining system performance based on usage patterns, allowing the SoC to improve efficiency over time and adapt to specific application demands.

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