AI Processor Power Tuning Through Compiler-Guided Parameter Optimization
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Solution Overview
Problem
Existing AI processors face challenges in achieving high performance with optimal power consumption, leading to issues such as heat generation and increased costs due to high power consumption.
Innovation Solution
A method involving a compiler that translates AI models into executable operations based on power and performance parameters, configures the AI processor, processes input data sets, generates power and performance data, and trains an AI algorithm to optimize power consumption by adjusting parameters for improved power management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If higher performance is achieved through increased computational resources and parallel processing, then processing speed and throughput are improved, but power consumption increases
Solution Approach 1:
The patent implements dynamic voltage and frequency scaling (DVFS) that adjusts operating parameters based on workload demands. The system monitors performance requirements and power consumption in real-time, dynamically adjusting voltage and frequency to match actual computational needs, thereby resolving the contradiction between maintaining high processing speed and reducing power consumption.
Solution Approach 2:
The invention changes physical parameters including voltage levels, frequency ranges, and operational modes based on workload characteristics. By implementing multiple voltage domains and frequency zones, the system can shift between high-performance high-power states and low-power states, effectively managing the trade-off between productivity and energy consumption.
2Productivity
If more computational resources are activated to handle larger data sets, then processing capability is improved, but heat generation increases
Solution Approach 1:
The patent divides the computational system into multiple independent processing clusters with separate power and thermal management. Each cluster can be independently activated or deactivated based on workload distribution, allowing heat generation to be localized and managed in specific regions rather than distributed throughout the entire system, thus resolving the contradiction between processing capability and heat generation.
Solution Approach 2:
The system implements periodic activation and deactivation of processing units based on workload patterns. During low-utilization periods, computational resources are put into sleep modes to reduce heat generation. The system periodically wakes active units only when computational tasks are queued, thereby maintaining processing capability while minimizing continuous heat generation.
3Loss of time
If parallel processing operations are increased to reduce latency, then performance is improved, but power consumption increases
Solution Approach 1:
The patent implements selective activation of processing units based on task priority and deadline requirements. For time-critical tasks with strict latency requirements, the system activates additional parallel processing units. For non-critical tasks, fewer units are activated. This partial action approach ensures that power consumption increases only when latency reduction is actually needed, resolving the contradiction between latency and power consumption.
Data Source
AI summary
In one embodiment, the present disclosure includes a method of reducing power in an artificial intelligence processor. For each cycle, over a plurality of cycles, an AI model is translated into operations executable on an artificial intelligence processor. The translating is based on power parameters that correspond to power consumption and performance of the artificial intelligence processor. The AI processor is configured with the executable operations, and input activation data sets are processed. Accordingly, result sets, power consumption data, and performance data are generated and stored over the plurality of cycles. The method further includes training an AI algorithm using the stored parameters, the power consumption data, and the performance data. A trained AI algorithm outputs a plurality of optimized parameters to reduce power consumption of the AI processor. The AI model is then translated into optimized executable operations based on the plurality of optimized parameters.