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