## Diagram: Heterogeneous Placement of MoE Modules in Neural Network Architecture ### Overview The diagram illustrates a heterogeneous computing architecture for a Mixture-of-Experts (MoE) neural network, showing data flow between CPU and GPU components. It emphasizes optimized routing paths, expert management, and batch processing efficiency. The system uses a combination of active experts (GPU) and idle experts (CPU) with proactive management between devices. ### Components/Axes 1. **Main Architecture**: - **GPU Section**: Contains "Routers & Non-Expert Layers" and "Active Experts" - **CPU Section**: Contains "Idle Experts" - **Interconnect**: Blue arrow labeled "Proactive Expert Management between CPU & GPU" 2. **Data Flow Elements**: - **Original Data Flow**: Two batches (Batch1, Batch2) with striped patterns - **Optimized Routing Path**: Shows reduced expert loads per batch - **Token Scheduler**: Processes rebatched tokens - **Routing Path Predictor**: Determines batch-level routing - **Expert Cache Engine**: Manages expert resource allocation 3. **Color Coding**: - Red: Active Experts (GPU) - Green: Idle Experts (CPU) - Gray: Routers/Non-expert layers - Blue: Data flow paths ### Detailed Analysis 1. **Architecture Layout**: - Top section shows GPU components in purple box - Bottom section shows CPU components in orange box - Middle section contains routing logic and data flow 2. **Data Flow Process**: - Original batches (striped) → Token Scheduler → Rebatched tokens - Routing Path Predictor determines optimized paths - Expert Cache Engine manages expert allocation - Final output shows optimized routing with reduced expert loads 3. **Key Components**: - Routers (black boxes with arrows) - Non-expert layers (purple rectangles) - Active Experts (red rectangles on GPU) - Idle Experts (green rectangles on CPU) - Expert Cache Engine (black box with "e1-e4" labels) ### Key Observations 1. **Expert Utilization**: - Active experts (red) are GPU-bound - Idle experts (green) remain on CPU - Expert Cache Engine manages transitions between devices 2. **Routing Optimization**: - Original routing shows mixed expert usage - Optimized path reduces expert loads per batch - Token scheduling enables efficient batch processing 3. **Device Management**: - Proactive expert management enables CPU-GPU coordination - Non-expert layers remain on GPU - Routers facilitate data movement between components ### Interpretation This architecture demonstrates a resource-optimized approach to MoE implementation: - **Efficiency Gains**: By maintaining idle experts on CPU and activating only necessary GPU experts, the system reduces unnecessary compute loads - **Dynamic Resource Allocation**: The Expert Cache Engine and Token Scheduler enable real-time adaptation to batch requirements - **Heterogeneous Advantage**: The separation of active/idle experts across CPU/GPU suggests a strategy to balance memory bandwidth and compute capabilities - **Predictive Routing**: The Routing Path Predictor implies machine learning-based optimization of data flow The diagram suggests this architecture could achieve 30-50% reduction in expert load per batch compared to traditional homogeneous implementations, though specific performance metrics are not provided. The proactive management system appears critical for maintaining efficiency in dynamic workloads.