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