- Standardize module.yaml files (11-13) to match concise format of early modules
- Remove verbose sections, keep essential metadata only
- Update kernels README to match TinyTorch module style standards
- Add comprehensive integration tests for kernels module
- Test hardware-optimized operations with real TinyTorch components
- Prepare for systematic integration testing across all modules
✅ **Full Module Implementation:**
- module.yaml: Proper metadata and dependencies
- README.md: Comprehensive documentation with learning objectives
- benchmarking_dev.py: Complete implementation with educational pattern
✅ **MLPerf-Inspired Architecture:**
- BenchmarkScenarios: Single-stream, server, and offline scenarios
- StatisticalValidator: Proper statistical validation and significance testing
- TinyTorchPerf: Complete framework integrating all components
- PerformanceReporter: Professional report generation for capstone projects
✅ **Educational Excellence:**
- Same structure as layers_dev.py with Build → Use → Analyze framework
- Comprehensive TODO guidance with step-by-step implementation
- Unit tests for each component with immediate feedback
- Integration testing with realistic TinyTorch models
- Professional module summary with career connections
✅ **Test Results:**
- All 5 test functions passing (100% success rate)
- Complete benchmarking workflow validated
- Statistical validation working correctly
- Professional reporting generating capstone-ready outputs
- Framework ready for student use
✅ **Capstone Preparation:**
- Students can now systematically evaluate their final projects
- Professional reporting suitable for academic presentations
- Statistical validation ensures meaningful results
- Industry-standard methodology following MLPerf patterns
🎓 **Perfect Bridge to Module 13 (MLOps):**
- Benchmarking establishes performance baselines
- MLOps will monitor production systems against these baselines
- Statistical validation transfers to production monitoring
- Professional reporting becomes production dashboards
