# Development Roadmap

## Mission
Create an intuitive visual pipeline designer that demonstrates clear speedup benefits of parallel NPU processing through real-time performance visualization and automated optimization.

## 🎯 Core Development Goals

### 1. Performance Visualization (Critical)
- **Speedup Metrics**: Clear display of 2x, 3x, 4x performance improvements
- **Before/After Comparison**: Visual proof of parallel processing benefits
- **Device Utilization**: Real-time visualization of NPU usage
- **Execution Flow**: Visual representation of parallel processing paths

### 2. Benchmarking System (Critical)
- **Automated Testing**: One-click performance measurement
- **Comparison Charts**: Single vs multi-device performance analysis
- **Regression Testing**: Track performance over time
- **Optimization Suggestions**: Automated recommendations

### 3. Device Management (High Priority)
- **Visual Dashboard**: Device status and health monitoring
- **Manual Allocation**: Drag-and-drop device assignment
- **Load Balancing**: Optimal distribution across available NPUs
- **Performance Profiling**: Individual device performance tracking

### 4. Real-time Monitoring (High Priority)
- **Live Charts**: FPS, latency, and throughput graphs
- **Resource Monitoring**: CPU, memory, and NPU utilization
- **Bottleneck Detection**: Automated identification of performance issues
- **Alert System**: Warnings for performance degradation

## 📋 Implementation Plan

### Phase 1: Performance Visualization (Weeks 1-2)

**Core Components:**
- `PerformanceBenchmarker` class for automated testing
- `PerformanceDashboard` widget with live charts
- Speedup calculation and display widgets
- Integration with existing pipeline editor

**Deliverables:**
- Single vs multi-device benchmark comparison
- Real-time FPS and latency monitoring
- Visual speedup indicators (e.g., "3.2x FASTER")
- Performance history tracking

### Phase 2: Device Management (Weeks 3-4)

**Core Components:**
- `DeviceManager` with enhanced NPU control
- `DeviceManagementPanel` for visual allocation
- Device health monitoring and profiling
- Load balancing optimization algorithms

**Deliverables:**
- Visual device status dashboard
- Drag-and-drop device assignment interface
- Device performance profiling and history
- Automatic load balancing recommendations

### Phase 3: Advanced Features (Weeks 5-6)

**Core Components:**
- `OptimizationEngine` for automated suggestions
- Pipeline analysis and bottleneck detection
- Configuration templates and presets
- Performance prediction algorithms

**Deliverables:**
- Automated pipeline optimization suggestions
- Configuration templates for common use cases
- Performance prediction before execution
- Bottleneck identification and resolution

### Phase 4: Professional Polish (Weeks 7-8)

**Core Components:**
- Advanced visualization and reporting
- Export and documentation features
- Performance analytics and insights
- User experience refinements

**Deliverables:**
- Professional performance reports
- Advanced analytics and trending
- Export capabilities for results
- Comprehensive user documentation

## 🎨 Target User Experience

### Ideal Workflow
1. **Design** (< 5 minutes): Drag-and-drop pipeline creation
2. **Configure**: Automatic device detection and optimal allocation
3. **Benchmark**: One-click performance measurement
4. **Monitor**: Real-time speedup visualization during execution
5. **Optimize**: Automated suggestions for performance improvements

### Success Metrics
- **Speedup Visibility**: Clear before/after performance comparison
- **Ease of Use**: Intuitive interface requiring minimal training
- **Performance Gains**: Measurable improvements from optimization
- **Professional Quality**: Enterprise-ready monitoring and reporting

## 🛠 Technical Approach

### Extend Current Architecture
- Build on existing `InferencePipeline` and `Multidongle` classes
- Enhance UI with new performance panels and dashboards
- Integrate visualization libraries (matplotlib/pyqtgraph)
- Add benchmarking automation and result storage

### Key Technical Components
- **Performance Engine**: Automated benchmarking and comparison
- **Visualization Layer**: Real-time charts and progress indicators
- **Device Abstraction**: Enhanced NPU management and allocation
- **Optimization Logic**: Automated analysis and suggestions

## 📈 Expected Impact

### For Users
- **Simplified Setup**: No coding required for parallel processing
- **Clear Benefits**: Visual proof of performance improvements
- **Optimal Performance**: Automated hardware utilization
- **Professional Tools**: Enterprise-grade monitoring and analytics

### For Platform
- **Competitive Advantage**: Unique visual approach to parallel AI inference
- **Market Expansion**: Lower barrier to entry for non-technical users
- **Performance Leadership**: Systematic optimization of NPU utilization
- **Enterprise Ready**: Foundation for advanced features and scaling