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Huawei H13-324_V2.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: CANN - ATC Model Conversion | 8% | - Conversion from mainstream frameworks to Ascend OM model - Model optimization and accuracy comparison - CANN architecture and functions - ATC tool usage and parameter configuration |
| Topic 2: CANN - Inference Application Development Process | 8% | - Application development workflow - Inference engine and API introduction - Data preprocessing and postprocessing - Multi-thread and batch processing design |
| Topic 3: Atlas AI Computing Solution | 13% | - Software stack and development environment - System composition and deployment modes - Atlas hardware product series and application scenarios |
| Topic 4: Comprehensive Experiment and Application Practice | 5% | - End-to-end AI application development based on Ascend - Problem analysis and solution design - Deployment and testing of actual projects |
| Topic 5: Neural Network Basics | 25% | - Model training, optimization and evaluation - Mathematical foundation for AI development - Common neural network structures and algorithms - Basic principles of deep learning |
| Topic 6: MindSpore Full-Process Development | 20% | - Network construction, training and inference - Automatic differentiation and parallel computing - MindSpore framework features and installation - Model saving, loading and deployment |
| Topic 7: CANN - Custom Operator Development | 8% | - Development, compilation and deployment of custom operators - Operator performance analysis and tuning - Operator development framework and specifications |
| Topic 8: Ascend AI Processor | 8% | - Memory management and data flow - Hardware specifications and computing resources - Architecture and features of Ascend AI processor |
| Topic 9: CANN - Inference Application Performance Tuning | 5% | - Data transmission and computing efficiency improvement - Performance indicators and analysis methods - System configuration and resource optimization |
