Dynamic Processor Frequency Scaling for Smart Camera Energy Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Real-time video transmission over unreliable communication networks often experiences image quality issues such as missing or distorted frames, freezing, and interruptions due to bandwidth fluctuations, packet losses, and limited energy capacity in smart camera systems.
Innovation Solution
Implementing dynamic frequency scaling of the processor's clock rate in smart camera systems based on the quality or bit rate of the video being streamed, utilizing a quality-to-frequency lookup table to adjust the bit rate and clock frequency for energy optimization, thereby enhancing energy efficiency and maintaining operational duration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the processor frequency is increased to maintain video streaming quality during network fluctuations, then video transmission reliability is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts the processor frequency based on real-time network conditions and video quality requirements. When network fluctuations occur, the frequency is increased to maintain quality; when conditions stabilize, frequency is reduced to save energy. This dynamic adaptation resolves the contradiction between reliability and energy consumption.
Solution Approach 2:
The invention changes the processor operating frequency parameter in response to varying network conditions and video quality metrics. By adjusting this key parameter, the system maintains video transmission reliability during critical moments while reducing energy consumption during stable periods, thus resolving the technical contradiction.
2Productivity
If the processor frequency is maintained at high levels to ensure real-time video processing, then video processing speed is improved, but operational duration decreases
Solution Approach 1:
The system employs periodic monitoring of network conditions and video quality metrics, adjusting processor frequency in periodic cycles rather than continuously. This periodic action maintains necessary processing speed when needed while allowing energy-saving states during stable periods, thereby extending operational duration without sacrificing critical video processing performance.
Solution Approach 2:
The processor frequency transitions between high and low states dynamically based on actual processing needs and network conditions. This dynamic adjustment ensures real-time video processing capability is maintained only when necessary, while extending operational duration through energy-saving low-frequency states during stable conditions.
3Manufacturing precision
If the video bit rate is increased to improve image quality, then image quality is improved, but energy consumption increases
Solution Approach 1:
The system adjusts the video bit rate parameter dynamically based on network conditions and quality requirements. When image quality is critical, the bit rate is increased; when network conditions are poor or energy is constrained, the bit rate is reduced. This parameter adaptation resolves the contradiction between image quality and energy consumption.
Solution Approach 2:
The video bit rate is dynamically adjusted in response to changing network conditions and quality requirements. This dynamic bit rate adaptation allows the system to maintain high image quality when necessary while reducing energy consumption during periods when lower quality is acceptable, thus resolving the technical contradiction.
4Use of energy by moving object
If dynamic frequency scaling is implemented to optimize energy consumption, then energy optimization is improved, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms that monitor network conditions, video quality metrics, and energy consumption levels. This feedback drives automatic frequency scaling decisions, optimizing energy consumption without requiring complex manual intervention. The feedback loop manages system complexity by using measured data to drive simple scaling decisions.
Solution Approach 2:
The dynamic frequency scaling system operates autonomously, using built-in monitoring of network conditions and video quality to automatically adjust processor frequency. This self-service approach optimizes energy consumption without requiring external control systems, managing complexity through self-regulation based on real-time conditions.
Data Source
AI summary
A smart camera system including an image sensor and a controller is presented. The image sensor generates video data that is initially at a bit rate of a pre-determined bit rate value. The controller is coupled to the image sensor to transmit the video data. The controller includes a processor operating at a clock rate of a first frequency. The processor is coupled to memory, the memory including instructions, which when executed by the controller causes the smart camera system to perform operations. The operations include dynamically scaling the clock rate of the processor to an adjustment frequency in response to receiving an input to change the bit rate of the video data. The adjustment frequency for the clock rate of the processor based, at least in part, on the input bit rate value. The operations further include changing the bit rate to the input bit rate value. The input bit rate value being different than the pre-determined bit rate value and the first frequency being different than the adjustment frequency.