Tag: HardwareOptimization

GPU Throttling

Posted on by lechuck park

GPU Throttling Architecture Analysis

This diagram illustrates the GPU’s power and thermal management system.

Key Components

1. Two Throttling Triggers

  • Power Throttling: Throttling triggered by power limits
  • Thermal Throttling: Throttling triggered by temperature limits

2. Different Control Approaches

  • Power Limit (Budget) Controller: Slow, Linear Step Down
  • Thermal Safety Controller: Fast, Hard Step Down
    • This aggressive response is necessary because overheating can cause immediate hardware damage

3. Priority Gate

Receives signals from both controllers and determines which limitation to apply.

4. PMU/SMU/DVFS Controller

The Common Control Unit that manages:

  • PMU: Power Management Unit
  • SMU: System Management Unit
  • DVFS: Dynamic Voltage and Frequency Scaling

5. Actual Adjustment Mechanisms

  • Clock Domain Controller: Reduces GPU Frequency
  • Voltage Regulator: Reduces GPU Voltage

6. Final Result

Lower Power/Temp (Throttled): Reduced power consumption and temperature in throttled state

Core Principle

When the GPU reaches power budget or temperature limits, it automatically reduces performance to protect the system. By lowering both frequency and voltage simultaneously, it effectively reduces power consumption (P ∝ V²f).

Summary

GPU throttling uses two controllers—power (slow, linear) and thermal (fast, aggressive)—that feed into a shared PMU/SMU/DVFS system to dynamically reduce clock frequency and voltage. Thermal throttling responds more aggressively than power throttling because overheating poses immediate hardware damage risks. The end result is lower power consumption and temperature, sacrificing performance to maintain system safety and longevity.

#GPUThrottling #ThermalManagement #PowerManagement #DVFS #GPUArchitecture #HardwareOptimization #ThermalSafety #PerformanceVsPower #ComputerHardware #GPUDesign #SystemManagement #ClockSpeed #VoltageRegulation #TechExplained #HardwareEngineering

With Claude

Flight LLM ( by FPGA )

Posted on by lechuck park

Flight LLM (FPGA) Analysis

This image is a technical document comparing “FlightLLM,” an FPGA-based LLM (Large Language Model) accelerator, with GPUs.

FlightLLM_FPGA Characteristics

Core Concept: An LLM inference accelerator utilizing Field-Programmable Gate Array, where SW developers become hardware architects, designing the exact circuit for the LLM.

Advantages vs Disadvantages Compared to GPU

✓ FPGA Advantages (Green Boxes)

1. Efficiency

  • High energy efficiency (~6x vs V100S)
  • Better cost efficiency (~1.8x TCO advantage)
  • Always-on-chip decoding
  • Maximized memory bandwidth utilization

2. Compute Optimization

  • Configurable sparse DSP(Digital Signal Processor) chains
  • DSP48-based sparse computation optimization
  • Efficient handling of diverse sparsity patterns

3. Compile/Deployment

  • Length-adaptive compilation
  • Significantly reduced compile overhead in real LLM services
  • High flexibility for varying sequence lengths

4. Architecture

  • Direct mapping of LLM sparsity & quantization
  • Efficient mapping onto heterogeneous FPGA memory tiers
  • Better utilization of bandwidth and capacity per tier

✗ FPGA Disadvantages (Orange Boxes)

1. Operating Frequency

  • Lower operating frequency (MHz-class)
  • Potential bottlenecks for less-parallel workloads

2. Development Time

  • Long compile/synthesis/P&R time
  • Slow development and iteration cycle

3. Development Complexity

  • High development complexity
  • Requires HDL/HLS-based design
  • Strong hardware/low-level optimization expertise needed

4. Portability Constraints

  • Limited generality (tied to specific compressed LLMs)
  • Requires redesign/recompile when switching models
  • Constrained portability and workload scalability

Key Trade-offs Summary

FPGAs offer superior energy and cost efficiency for specific LLM workloads but require significantly higher development expertise and have lower flexibility compared to GPUs. They excel in massive, fixed parallel workloads but struggle with rapid model iteration and portability.

FlightLLM leverages FPGAs to achieve 6x energy efficiency and 1.8x cost advantage over GPUs through direct hardware mapping of LLM operations. However, this comes at the cost of high development complexity, requiring HDL/HLS expertise and long compilation times. FPGAs are ideal for production deployments of specific LLM models where efficiency outweighs the need for flexibility and rapid iteration.

#FPGA #LLM #AIAccelerator #FlightLLM #HardwareOptimization #EnergyEfficiency #MLInference #CustomHardware #AIChips #DeepLearningHardware

With Claude