Processing Unit DVFS With ML Workload and Thermal Balancing
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Solution Overview
Problem
Traditional dynamic voltage and frequency scaling (DVFS) systems in semiconductor IC devices face limitations in adapting to real-time workload fluctuations, particularly in applications like AI and ML, leading to suboptimal performance and excessive power consumption due to reliance on preset values and inadequate thermal management.
Innovation Solution
A power-performance balance system utilizing a machine learning model, such as a recurrent neural network, to dynamically adjust voltage and frequency based on real-time workload analysis, thermal profiling, and cooling mechanisms to optimize performance and power efficiency by identifying critical path functional blocks and adjusting operating frequencies and voltages accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional DVFS systems use preset values for voltage and frequency scaling, then device complexity is reduced, but adaptability to real-time workload fluctuations deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where performance counters and sensors continuously monitor workload characteristics, power consumption, and temperature. This real-time data feeds back to the DVFS controller, which dynamically adjusts voltage and frequency scaling decisions. The feedback loop enables the system to adapt to changing workload conditions while maintaining manageable device complexity through automated control.
Solution Approach 2:
The patent transitions from static preset DVFS values to dynamic scaling decisions based on real-time workload analysis. The system continuously adapts voltage and frequency levels according to actual performance needs, power consumption patterns, and thermal conditions. This dynamic approach allows the processing units to optimize performance for varying workload types without requiring complex manual configuration.
2Productivity
If voltage and frequency are scaled up to handle increasing workloads, then productivity is improved, but power consumption increases
Solution Approach 1:
The patent dynamically changes operating parameters (voltage and frequency) based on actual workload requirements rather than maintaining fixed high-performance settings. By adjusting these parameters in real-time according to performance counters and sensor data, the system achieves high productivity when needed while reducing power consumption during lower-demand periods. This parameter optimization resolves the trade-off between processing capacity and energy usage.
Solution Approach 2:
The patent applies partial scaling actions by adjusting voltage and frequency to the minimum necessary levels to meet current workload demands. Rather than consistently operating at maximum capacity, the system applies just enough scaling to handle the actual workload, avoiding excessive power consumption. This partial action approach maintains productivity while optimizing energy efficiency.
3Adaptability or versatility
If dynamic voltage and frequency scaling is implemented without thermal management, then adaptability to workload is improved, but thermal stress increases
Solution Approach 1:
The patent integrates thermal sensors and performance counters that provide continuous feedback on temperature conditions alongside workload characteristics. This feedback enables the DVFS controller to make informed decisions that balance performance adaptation with thermal management. When thermal stress is detected, the system can reduce scaling aggressiveness or adjust operating points to maintain adaptability while preventing excessive thermal accumulation.
Solution Approach 2:
The patent implements preventive thermal management by monitoring temperature trends and adjusting voltage/frequency scaling before critical thermal thresholds are reached. By anticipating thermal stress conditions through continuous sensing and proactive scaling adjustments, the system maintains workload adaptability while preventing harmful thermal accumulation. This cushioning approach allows aggressive scaling when thermal conditions permit while protecting against thermal stress.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively balances power and performance by adaptively scaling voltage and frequency, reducing thermal stress and resource inefficiencies, thereby enhancing efficiency and thermal management in semiconductor IC devices.
Implementation Method 1
adjusting the temperature-dependent operating frequency to a target operating frequency having a value within a predetermined difference from the temperature-independent theoretical operating frequency by actively cooling the processing unit using a cooling unit coupled to the processing unit
Data Source
AI summary
Disclosed are system and methods of dynamically balancing power and performance of a processing unit. The method includes receiving workloads to be processed by the processing unit; classifying, using a recurrent neural network, the workloads according to expected resources to be expended by different functional blocks; identifying a critical path workload based on the classification of workloads; determining a temperature-independent operating frequency for the critical path workload; determining a temperature-dependent operating frequency for the critical path workload based on a junction temperature of the processing unit; adjusting the temperature-dependent operating frequency to a target operating frequency having a value within a predetermined difference from the temperature-independent operating frequency by actively cooling the processing unit; setting a critical path supply voltage based on the target operating frequency; and setting a non-critical path voltage for remaining ones of the workloads at a value less than the critical path supply voltage.