AI CPU Clock Control for Communication Processor Workloads
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Solution Overview
Problem
Existing electronic devices face challenges in managing CPU clock levels to optimize power consumption and heat generation, particularly in communication processors, which can lead to increased current consumption and heat generation due to varying workloads and data throughput.
Innovation Solution
An electronic device employs an AI model trained on CPU utilization and bus traffic information to dynamically adjust CPU clock levels based on workload identification, allowing for intelligent control of CPU clocks in communication processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CPU clock is increased to handle larger number of processes and higher data throughput, then processing capability and communication performance are improved, but power consumption and heat generation increase
Solution Approach 1:
The patent applies dynamic voltage and frequency scaling (DVFS) to dynamically adjust CPU clock frequency based on actual workload requirements. The system transitions from static clocking to dynamic clocking, where the CPU clock is continuously adjusted according to process quantity and data throughput conditions, thereby optimizing the balance between processing capability and power consumption.
Solution Approach 2:
The patent changes the CPU clock frequency parameter dynamically based on workload conditions. By monitoring the number of processes and data throughput, the system adjusts the clock frequency parameter to match actual demands, reducing unnecessary high-power consumption when processing requirements are low while maintaining adequate performance when needed.
2Productivity
If CPU clock is increased to handle larger number of processes and higher data throughput, then processing capability and communication performance are improved, but heat generation increases
Solution Approach 1:
The system dynamically adjusts CPU clock frequency based on actual processing needs, preventing sustained high-clock operation that causes excessive heat generation. By matching clock frequency to workload intensity, the system maintains processing capability while controlling thermal output.
Solution Approach 2:
The patent modifies the CPU clock frequency parameter in response to changing workload conditions, thereby controlling heat generation. When processing demands are low, the clock frequency is reduced, directly lowering heat generation while preserving the ability to increase performance when needed.
3Use of energy by moving object
If AI model is introduced for intelligent clock control, then power consumption optimization is improved, but device complexity increases
Solution Approach 1:
The patent implements a self-service control mechanism where the system automatically monitors its own workload (number of processes, data throughput) and adjusts CPU clock frequency without external intervention. The built-in monitoring and control functions enable the system to self-regulate power consumption based on actual processing demands.
Solution Approach 2:
The system incorporates feedback mechanisms where workload information (process count, data throughput) is continuously monitored and fed back to the clock control function. This feedback loop enables dynamic adjustment of CPU clock frequency based on actual performance conditions, optimizing power consumption through closed-loop control.
Data Source
AI summary
According to an embodiment, an electronic device may include at least one communication processor comprising at least one central processing unit (CPU). According to an embodiment, the at least one communication processor may be configured to enter an RRC_Connected state. According to an embodiment, the at least one communication processor may be configured to control a clock level for the at least one CPU to be a first CPU clock level corresponding to the RRC_Connected state. According to an embodiment, the at least one communication processor may be configured to identify workload information comprising at least one of utilization information of the at least one CPU and traffic information of at least one bus of the at least one communication processor. According to an embodiment, the at least one communication processor may be configured provide the workload information as an input to an artificial intelligence (AI) model, wherein the AI model is trained using training data comprising at least one of CPU utilization information and bus traffic information as input values and CPU clock levels as output values. According to an embodiment, the at least one communication processor may be configured to identify, based on the providing the workload information as the input to the AI model, a second CPU clock level as an output of the AI model. Various other embodiments may be possible.