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Huawei H13-324_V1.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: CANN Inference Performance Tuning | 5% | - Performance analysis tools - Common tuning strategies - Memory and computing optimization |
| Topic 2: Neural Network Fundamentals | 25% | - Basic deep learning theory - Model training and optimization methods - Common neural network structures - Model compression and quantization basics |
| Topic 3: MindSpore Full-Process Development | 15% | - MindSpore framework architecture - Model saving, exporting and deployment - Model building, training and validation |
| Topic 4: CANN Inference Application Development | 15% | - Multi-thread and multi-device inference - AscendCL API and development flow - Data preprocessing and postprocessing |
| Topic 5: Ascend AI Processor | 8% | - Architecture and features of Ascend AI processor - Memory and storage system - Core components and computing units |
| Topic 6: Atlas AI Computing Solutions | 13% | - System software and toolchain overview - Application scenarios and deployment modes - Atlas series hardware products |
| Topic 7: CANN-ATC Model Conversion | 12% | - ATC tool functions and parameters - Conversion from mainstream frameworks to Ascend OM model - Troubleshooting conversion errors - CANN platform overview |
| Topic 8: CANN Custom Operator Development | 7% | - Operator implementation and compilation - Operator verification and integration - Operator development specification |
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