# Cluster4NPU UI - Project Summary

## Vision

Create an intuitive visual tool that enables users to design parallel AI inference pipelines for Kneron NPU dongles without coding knowledge, with clear visualization of performance benefits and hardware utilization.

## Current System Status

### ✅ Current Capabilities

**Visual Pipeline Designer:**
- Drag-and-drop node-based interface using NodeGraphQt
- 5 node types: Input, Model, Preprocess, Postprocess, Output
- Real-time pipeline validation and stage counting
- Property configuration panels with type-aware widgets
- Pipeline persistence in .mflow JSON format

**Professional UI:**
- Three-panel layout (templates, editor, configuration)
- Global status bar with live statistics
- Real-time connection analysis and error detection
- Integrated project management and recent files

**Inference Engine:**
- Multi-stage pipeline orchestration with threading
- Kneron NPU dongle integration (KL520, KL720, KL1080)
- Hardware auto-detection and device management
- Real-time performance monitoring (FPS, latency)

### 🎯 Core Use Cases

**Pipeline Flow:**
```
Input → Preprocess → Model → Postprocess → Output
  ↓        ↓          ↓         ↓          ↓
Camera   Resize   NPU Inference  Format   Display
```

**Supported Sources:**
- USB cameras with configurable resolution/FPS
- Video files (MP4, AVI, MOV) with frame processing
- Image files (JPG, PNG, BMP) for batch processing
- RTSP streams for live video (basic support)

## 🚀 Development Priorities

### Immediate Goals
1. **Performance Visualization**: Show clear speedup benefits of parallel processing
2. **Device Management**: Enhanced control over NPU dongle allocation
3. **Benchmarking System**: Automated performance testing and comparison
4. **Real-time Dashboard**: Live monitoring of pipeline execution

## 🚨 Key Missing Features

### Performance Visualization
- Parallel vs sequential execution comparison
- Visual device allocation and load balancing
- Speedup calculation and metrics display
- Performance improvement charts

### Advanced Monitoring
- Live performance graphs (FPS, latency, throughput)
- Resource utilization visualization
- Bottleneck identification and alerts
- Historical performance tracking

### Device Management
- Visual device status dashboard
- Manual device assignment interface
- Device health monitoring and profiling
- Optimal allocation recommendations

### Pipeline Optimization
- Automated benchmark execution
- Performance prediction before deployment
- Configuration templates for common use cases
- Optimization suggestions based on analysis

## 🛠 Technical Architecture

### Current Foundation
- **Core Processing**: `InferencePipeline` with multi-stage orchestration
- **Hardware Integration**: `Multidongle` with NPU auto-detection
- **UI Framework**: PyQt5 with NodeGraphQt visual editor
- **Pipeline Analysis**: Real-time validation and stage detection

### Key Components Needed
1. **PerformanceBenchmarker**: Automated speedup measurement
2. **DeviceManager**: Advanced NPU allocation and monitoring
3. **VisualizationDashboard**: Live performance charts and metrics
4. **OptimizationEngine**: Automated configuration suggestions

## 🎯 Implementation Roadmap

### Phase 1: Performance Visualization
- Implement parallel vs sequential benchmarking
- Add speedup calculation and display
- Create performance comparison charts
- Build real-time monitoring dashboard

### Phase 2: Device Management
- Visual device allocation interface
- Device health monitoring and profiling
- Manual assignment capabilities
- Load balancing optimization

### Phase 3: Advanced Features
- Pipeline optimization suggestions
- Configuration templates
- Performance prediction
- Advanced analytics and reporting

## 🎨 User Experience Goals

### Target Workflow
1. **Design**: Drag-and-drop pipeline creation (< 5 minutes)
2. **Configure**: Automatic device detection and allocation
3. **Preview**: Performance prediction before execution
4. **Monitor**: Real-time speedup visualization
5. **Optimize**: Automated suggestions for improvements

### Success Metrics
- Clear visualization of parallel processing benefits
- Intuitive interface requiring minimal training
- Measurable performance improvements from optimization
- Professional-grade monitoring and analytics

## 📈 Business Value

**For Users:**
- No-code parallel processing setup
- Clear ROI demonstration through speedup metrics
- Optimal hardware utilization without expert knowledge

**For Platform:**
- Unique visual approach to AI inference optimization
- Lower barrier to entry for complex parallel processing
- Scalable foundation for enterprise features