CV Manager Power Optimization for Computer Vision Workloads
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Solution Overview
Problem
Computer vision environments face inefficiencies and power-related issues due to suboptimal resource allocation and consumption, leading to potential failures and reduced performance.
Innovation Solution
A system and method involving a CV manager that generates power optimization reports based on workload and environment information, dynamically reallocating workloads to optimize power usage across CV nodes, using power and GPU partition optimization models to ensure efficient resource allocation and prevent failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If power optimization is implemented in CV environments, then power consumption is reduced and efficiency is improved, but system complexity increases due to dynamic workload reallocation and optimization models
Solution Approach 1:
A CV manager is introduced as an intermediary component that handles power optimization tasks, workload reallocation, and coordination between CV nodes. This mediator abstracts the complexity of power management from individual CV nodes, centralizing control logic while reducing overall system complexity through specialized management.
Solution Approach 2:
The system implements feedback mechanisms where power consumption data and workload performance metrics are continuously monitored and fed back to the CV manager. This feedback loop enables dynamic adjustment of workload allocation to optimize power consumption while maintaining performance requirements, resolving the contradiction between energy efficiency and system complexity.
2Use of energy by moving object
If dynamic workload reallocation is performed to optimize power usage, then power efficiency improves, but processing speed may be affected due to reallocation overhead
Solution Approach 1:
The system implements dynamic workload reallocation that adapts to changing power conditions and performance requirements. Workloads are dynamically assigned to CV nodes based on real-time power consumption patterns and processing capabilities, enabling the system to optimize power efficiency without permanently sacrificing processing speed through rigid allocation.
Solution Approach 2:
The CV manager changes workload allocation parameters dynamically based on power optimization goals and performance metrics. By adjusting allocation parameters rather than fundamentally restructuring the system, the patent achieves power efficiency improvements while minimizing the impact on processing speed through controlled parameter modifications.
3Reliability
If power optimization models are used to manage CV workloads, then reliability improves by preventing power failures, but measurement and analysis complexity increases
Solution Approach 1:
The patent replaces complex manual power management and analysis with automated software-based power optimization models and monitoring systems. These computational models automatically analyze power consumption patterns, predict potential failures, and implement preventive measures, reducing the difficulty of measurement and analysis while improving reliability through consistent automated monitoring.
Data Source
AI summary
Techniques described herein relate to a method for optimizing power for a computer vision environment. The method includes obtaining, by a computer vision (CV) manager, an initial power optimization request associated with a CV workload; in response to obtaining the initial power optimization request: obtaining CV workload information associated with the CV workload; obtaining first CV environment configuration information associated with the power optimization request; generating a power optimization report based on the first CV environment configuration information and the CV workload information using a power optimization model; and initiating performance of the CV workload in a CV environment based on the power optimization report.