Hardware Processor Workload Prediction for Dynamic Performance Adaptation
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Solution Overview
Problem
Existing hardware processors have static performance tuning based on anticipated workloads, which does not adapt to real-time or changing workloads, leading to inefficiencies in power consumption and performance optimization.
Innovation Solution
A workload prediction system within the hardware processor uses telemetry data and a machine learning model to predict the current workload, selecting an optimal profile to adjust operating parameters such as voltage and frequency in real-time, enhancing performance and power management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static performance tuning is used based on anticipated workloads, then device complexity is reduced and ease of operation is improved, but adaptability to changing workloads deteriorates and productivity decreases
Solution Approach 1:
The patent implements dynamic performance tuning by continuously monitoring actual workload characteristics and adjusting operating parameters (voltage, frequency, clock speed) in real-time. The system transitions from static pre-configured performance levels to dynamic adaptation where the hardware processor automatically adjusts its performance characteristics based on measured workload patterns, resolving the contradiction between ease of operation and adaptability.
Solution Approach 2:
The patent employs feedback mechanisms where the hardware processor monitors actual workload execution characteristics and uses this information to adjust performance parameters. The system compares actual workload patterns against anticipated workloads and modifies operating parameters accordingly, creating a closed-loop control system that maintains ease of operation while improving adaptability through automatic performance optimization.
2Productivity
If performance is increased to handle complex workloads, then productivity is improved, but use of energy deteriorates
Solution Approach 1:
The patent dynamically adjusts key operating parameters including voltage, frequency, and clock speed based on actual workload characteristics. By changing these parameters in real-time according to workload demands, the system achieves high productivity for complex workloads while reducing power consumption during simpler tasks, effectively resolving the contradiction between productivity and energy usage.
Solution Approach 2:
The system implements dynamic performance scaling where operating parameters are continuously adjusted based on monitored workload characteristics. This dynamic approach allows the hardware processor to allocate computational resources and energy consumption proportional to actual workload requirements, achieving optimal productivity-energy efficiency tradeoff rather than maintaining fixed high performance regardless of workload complexity.
3Use of energy by moving object
If performance is reduced to conserve power, then use of energy is improved, but productivity deteriorates
Solution Approach 1:
The patent dynamically modifies operating parameters such as voltage and frequency based on actual workload characteristics. This enables the system to reduce power consumption during low-complexity tasks while maintaining high productivity when complex workloads are detected, resolving the contradiction between energy efficiency and productivity through data-driven parameter adjustment.
Solution Approach 2:
The system uses feedback from workload monitoring to automatically adjust performance levels. By continuously measuring actual workload characteristics and comparing them against power consumption thresholds, the system can reduce productivity (performance) only when necessary to conserve power, while maintaining high productivity when workload characteristics indicate demand, thus resolving the contradiction through intelligent feedback-based control.
4Productivity
If operating parameters are optimized for specific workloads, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service performance optimization where the hardware processor automatically monitors its own workload characteristics and adjusts operating parameters without external intervention. The system self-tunes performance based on measured workload patterns, achieving workload-specific optimization while avoiding the need for complex manual configuration or external control mechanisms, thus resolving the contradiction between productivity optimization and device complexity.
Solution Approach 2:
The system employs feedback-based automatic tuning where operating parameters are adjusted based on monitored workload characteristics. This self-regulating mechanism enables the hardware processor to optimize performance for specific workloads autonomously, achieving high productivity without requiring complex external control systems or manual parameter configuration, thereby resolving the contradiction between productivity and device complexity.
Data Source
AI summary
Performance adaptation for an integrated circuit includes receiving, by a workload prediction system of a hardware processor, telemetry data for one or more systems of the hardware processor. A workload prediction is determined by processing the telemetry data through a workload prediction model executed by a workload prediction controller of the workload prediction system. A profile is selected, from a plurality of profiles, that matches the workload prediction. The selected profile specifies one or more operating parameters for the hardware processor. The selected profile is provided to a power management controller of the hardware processor for controlling an operational characteristic of the one or more systems.