This image illustrates the DVFS (Dynamic Voltage and Frequency Scaling) system workflow, which is a power management technique that dynamically adjusts CPU/GPU voltage and frequency to optimize power consumption.
Key Components and Operation Flow
1. Main Process Flow (Top Row)
- Workload Init → Workload Analysis → DVFS Policy Decision → Clock Frequency Adjustment → Voltage Adjustment → Workload Execution → Workload Finish
2. Core System Components
Power State Management:
- Basic power states: P0~P12 (P0 = highest performance, P12 = lowest power)
- Real-time monitoring through PMU (Power Management Unit)
Analysis & Decision Phase:
- Applies dynamic power consumption formula using algorithms
- Considers thermal limits in analysis
- Selects new power state (High: P0-P2, Low: P8-P10)
- P-State changes occur within 10μs~1ms
Frequency Adjustment (PLL – Phase-Locked Loop):
- Adjusts GPU core and memory clock frequencies
- Typical range: 1,410MHz~1,200MHz (memory), 1,000MHz~600MHz (core)
- Adjustment time: 10-100 microseconds
Voltage Adjustment (VRM – Voltage Regulator Module):
- Adjusts voltage supplied to GPU core and memory
- Typical range: 1.1V (P0) to 0.8V (P8)
- VRM stabilizes voltage within tens of microseconds
3. Real-time Feedback Loop
The system operates a continuous feedback loop that readjusts P-states in real-time based on workload changes, maintaining optimal balance between performance and power efficiency.
4. Execution Phase
The GPU executes workloads at new frequency and voltage settings, with asynchronous adjustments based on frequency and voltage changes. After completion, the system transitions to low-power states (e.g., P10, P12) to conserve energy.
Summary: Key Benefits of DVFS
DVFS technology is for AI data centers as it optimizes GPU efficiency management to achieve maximum overall power efficiency. By intelligently scaling thousands of GPUs based on AI workload demands, DVFS can reduce total data center power consumption by 30-50% while maintaining peak AI performance during training and inference operations, making it essential for sustainable and cost-effective AI infrastructure at scale.
With Claude
Lechuck Park 