Graphics Microcontroller Power State Prediction
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Solution Overview
Problem
Existing power management techniques for graphics processors face challenges in balancing performance and power consumption, particularly in scenarios where reducing power consumption diminishes performance and continuously powering a GPU can increase energy usage, making it sub-optimal for battery-powered or low-power devices.
Innovation Solution
A method that utilizes a graphics microcontroller to analyze work history and create a probability distribution function to predict when a graphics processor can power down or power up, transitioning between power states based on latency values and idle periods to optimize power usage without sacrificing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the GPU is continuously powered to promptly process work from the CPU, then performance is improved, but power consumption increases
Solution Approach 1:
The system performs preliminary actions by predicting future work arrivals using probability distribution functions before the GPU actually needs to process them. This allows the GPU to be proactively powered down or kept in low-power states when work is unlikely to arrive, while ensuring it's powered on in time when work is predicted to arrive, thus resolving the contradiction between continuous operation for performance and power down for energy savings.
2Use of energy by moving object
If the GPU is powered down during idle periods to save power, then power consumption is reduced, but performance is diminished due to power-on delays
Solution Approach 1:
The system performs preliminary power management actions by using the probability distribution function to predict when the GPU will need to process work. This predictive approach allows the system to time power transitions optimally - powering down when idle periods are predicted and powering on with sufficient lead time before work arrives, thereby avoiding performance delays while maximizing power savings.
Solution Approach 2:
The system dynamically adjusts the GPU power state based on predicted workload patterns rather than using static on/off decisions. The probability distribution function provides a dynamic model that adapts to varying work arrival patterns, allowing the GPU to transition between power states at optimal times that balance power consumption and performance requirements.
3Productivity
If voltage and frequency are increased to boost GPU throughput, then performance is improved, but power consumption increases
Solution Approach 1:
The system performs preliminary analysis of work history and creates probability distribution functions to predict future workload patterns. This predictive capability allows the system to proactively adjust voltage and frequency settings before work arrives, enabling high-performance modes only when work is predicted to arrive, and low-power modes when idle periods are predicted, thus resolving the contradiction between high throughput and power consumption.
Data Source
AI summary
Systems, methods, and computer readable media to manage power for a graphics processor are described. When the power management component determines the graphics processor is idle when processing a current frame by the graphics processor, the power management component predicts an idle period for the graphics processor based on the work history. The power management component obtains a first latency value indicative of a power on time period and a second latency value indicative of a power off time period for a graphics processor component, such as graphics processor hardware. The power management component provides power instructions to transition the graphics processor component to the power off state based on a determination that a combined latency value of the first latency value and the second latency value is less than the idle period.