131 lines
4.9 KiB
Markdown
131 lines
4.9 KiB
Markdown
# Development Roadmap
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## Mission
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Create an intuitive visual pipeline designer that demonstrates clear speedup benefits of parallel NPU processing through real-time performance visualization and automated optimization.
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## 🎯 Core Development Goals
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### 1. Performance Visualization (Critical)
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- **Speedup Metrics**: Clear display of 2x, 3x, 4x performance improvements
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- **Before/After Comparison**: Visual proof of parallel processing benefits
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- **Device Utilization**: Real-time visualization of NPU usage
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- **Execution Flow**: Visual representation of parallel processing paths
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### 2. Benchmarking System (Critical)
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- **Automated Testing**: One-click performance measurement
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- **Comparison Charts**: Single vs multi-device performance analysis
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- **Regression Testing**: Track performance over time
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- **Optimization Suggestions**: Automated recommendations
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### 3. Device Management (High Priority)
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- **Visual Dashboard**: Device status and health monitoring
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- **Manual Allocation**: Drag-and-drop device assignment
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- **Load Balancing**: Optimal distribution across available NPUs
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- **Performance Profiling**: Individual device performance tracking
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### 4. Real-time Monitoring (High Priority)
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- **Live Charts**: FPS, latency, and throughput graphs
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- **Resource Monitoring**: CPU, memory, and NPU utilization
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- **Bottleneck Detection**: Automated identification of performance issues
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- **Alert System**: Warnings for performance degradation
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## 📋 Implementation Plan
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### Phase 1: Performance Visualization (Weeks 1-2)
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**Core Components:**
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- `PerformanceBenchmarker` class for automated testing
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- `PerformanceDashboard` widget with live charts
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- Speedup calculation and display widgets
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- Integration with existing pipeline editor
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**Deliverables:**
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- Single vs multi-device benchmark comparison
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- Real-time FPS and latency monitoring
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- Visual speedup indicators (e.g., "3.2x FASTER")
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- Performance history tracking
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### Phase 2: Device Management (Weeks 3-4)
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**Core Components:**
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- `DeviceManager` with enhanced NPU control
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- `DeviceManagementPanel` for visual allocation
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- Device health monitoring and profiling
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- Load balancing optimization algorithms
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**Deliverables:**
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- Visual device status dashboard
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- Drag-and-drop device assignment interface
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- Device performance profiling and history
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- Automatic load balancing recommendations
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### Phase 3: Advanced Features (Weeks 5-6)
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**Core Components:**
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- `OptimizationEngine` for automated suggestions
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- Pipeline analysis and bottleneck detection
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- Configuration templates and presets
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- Performance prediction algorithms
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**Deliverables:**
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- Automated pipeline optimization suggestions
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- Configuration templates for common use cases
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- Performance prediction before execution
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- Bottleneck identification and resolution
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### Phase 4: Professional Polish (Weeks 7-8)
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**Core Components:**
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- Advanced visualization and reporting
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- Export and documentation features
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- Performance analytics and insights
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- User experience refinements
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**Deliverables:**
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- Professional performance reports
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- Advanced analytics and trending
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- Export capabilities for results
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- Comprehensive user documentation
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## 🎨 Target User Experience
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### Ideal Workflow
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1. **Design** (< 5 minutes): Drag-and-drop pipeline creation
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2. **Configure**: Automatic device detection and optimal allocation
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3. **Benchmark**: One-click performance measurement
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4. **Monitor**: Real-time speedup visualization during execution
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5. **Optimize**: Automated suggestions for performance improvements
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### Success Metrics
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- **Speedup Visibility**: Clear before/after performance comparison
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- **Ease of Use**: Intuitive interface requiring minimal training
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- **Performance Gains**: Measurable improvements from optimization
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- **Professional Quality**: Enterprise-ready monitoring and reporting
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## 🛠 Technical Approach
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### Extend Current Architecture
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- Build on existing `InferencePipeline` and `Multidongle` classes
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- Enhance UI with new performance panels and dashboards
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- Integrate visualization libraries (matplotlib/pyqtgraph)
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- Add benchmarking automation and result storage
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### Key Technical Components
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- **Performance Engine**: Automated benchmarking and comparison
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- **Visualization Layer**: Real-time charts and progress indicators
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- **Device Abstraction**: Enhanced NPU management and allocation
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- **Optimization Logic**: Automated analysis and suggestions
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## 📈 Expected Impact
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### For Users
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- **Simplified Setup**: No coding required for parallel processing
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- **Clear Benefits**: Visual proof of performance improvements
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- **Optimal Performance**: Automated hardware utilization
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- **Professional Tools**: Enterprise-grade monitoring and analytics
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### For Platform
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- **Competitive Advantage**: Unique visual approach to parallel AI inference
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- **Market Expansion**: Lower barrier to entry for non-technical users
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- **Performance Leadership**: Systematic optimization of NPU utilization
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- **Enterprise Ready**: Foundation for advanced features and scaling |