Dynamic Power Proxy Weight Tuning for Microprocessor Cores
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Solution Overview
Problem
Existing power proxy architectures in microprocessors rely on static calibration methods, which do not adapt to changing application phases or performance feedback, leading to inaccurate power consumption estimation and management.
Innovation Solution
The dynamic adaptation of power proxy architectures by adjusting programmable weights and models based on real-time application conditions, using online computation and feedback to optimize power usage estimates and adjust operational parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static calibration methodology is used to set power proxy weights, then the calibration process is simple and systematic, but the power consumption estimation accuracy deteriorates over time as application phases change
Solution Approach 1:
The patent applies dynamics by transitioning from static calibration to dynamic runtime adaptation. The power proxy architecture now adjusts its weightings and operational parameters in real-time based on feedback about actual power consumption patterns, allowing it to adapt to changing application phases and hardware states throughout the processor's lifetime.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors actual power consumption and compares it against predictions from the power proxy model. This feedback loop enables the system to refine its calibration online, correcting drift and adapting to new workload patterns without requiring manual recalibration.
2Device complexity
If programmable weights are fixed at initial program load time, then the architecture is simpler to implement, but adaptability to changing application conditions deteriorates
Solution Approach 1:
The system transitions from fixed weights set at IPL to dynamic weights that can be adjusted at runtime. This allows the power proxy to adapt to different application phases, workload characteristics, and hardware states without requiring a complete architectural redesign, balancing complexity with adaptability.
Solution Approach 2:
The patent changes the parameters (weights and operational thresholds) of the power proxy architecture from fixed values to dynamically adjustable parameters. This enables the system to optimize its power estimation accuracy for different scenarios while maintaining a relatively simple base architecture that evolves through parameter updates rather than structural changes.
3Productivity
If a pre-fixed linear model is used for power estimation, then the computation is faster and simpler, but measurement precision deteriorates due to model inaccuracies under varying conditions
Solution Approach 1:
The system incorporates feedback mechanisms that continuously refine the linear model parameters based on actual power consumption measurements. This allows the simple linear model structure to maintain high accuracy by dynamically adjusting its parameters, combining computational efficiency with measurement precision through online calibration.
Solution Approach 2:
The patent applies parameter changes by updating the coefficients and operational parameters of the linear model in runtime based on observed power consumption patterns. This enables the model to adapt to changing conditions while maintaining the computational simplicity of linear estimation, achieving both speed and accuracy.
4Measurement precision
If runtime adaptation of power proxy weights is implemented, then power consumption estimation accuracy improves, but device complexity increases due to additional control logic
Solution Approach 1:
The system uses feedback from power consumption measurements to automatically adjust proxy weights, reducing the need for complex manual calibration logic. The feedback-driven automatic tuning simplifies control by using the data itself to guide adjustments rather than requiring sophisticated external control algorithms.
Solution Approach 2:
The power proxy architecture performs self-calibration and self-adjustment using feedback about its own performance. This self-service capability eliminates the need for complex external control logic, as the system automatically tunes its parameters based on observed power consumption patterns and model accuracy.
Data Source
AI summary
A mechanism is provided for automatically tuning power proxy architectures. Based on the set of conditions related to an application being executed on a microprocessor core, a weight factor to use for each activity in a set of activities being monitored for the microprocessor core is identified, thereby forming a set of weight factors. A power usage estimate value is generated using the set of activities and the set of weight factors. A determination is made as to whether the power usage estimate value is greater than a power proxy threshold value identifying a maximum power usage for the microprocessor core. Responsive to the power usage estimate value being greater than the power proxy threshold value, a set of signals is sent to one or more on-chip actuators in the power proxy unit associated with the microprocessor core and a set of operational parameters associated with the component are adjusted.