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Course Outline
Introduction to Edge AI and Ascend 310
- Edge AI Overview: trends, constraints, and applications
- Architecture of the Huawei Ascend 310 chip and its supported toolchain
- The role of CANN in the edge AI deployment stack
Model Preparation and Conversion
- Exporting trained models from TensorFlow, PyTorch, and MindSpore
- Utilizing ATC to convert models to OM format for Ascend devices
- Addressing unsupported operations and strategies for lightweight conversion
Building Inference Pipelines with AscendCL
- Executing OM models on Ascend 310 using the AscendCL API
- Input/output preprocessing, memory management, and device control
- Deployment within embedded containers or lightweight runtime environments
Optimization for Edge Constraints
- Reducing model size and tuning precision (FP16, INT8)
- Identifying bottlenecks using the CANN profiler
- Managing memory layout and data streaming to boost performance
Deploying with MindSpore Lite
- Using the MindSpore Lite runtime for mobile and embedded targets
- Comparing MindSpore Lite against raw AscendCL pipelines
- Packaging inference models for device-specific deployment
Edge Deployment Scenarios and Case Studies
- Case study: implementing object detection with a smart camera on Ascend 310
- Case study: real-time classification in an IoT sensor hub
- Monitoring and updating deployed models at the edge
Conclusion and Future Directions
Requirements
- Practical experience with AI model development or deployment processes
- Fundamental understanding of embedded systems, Linux, and Python
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch
Target Audience
- IoT solution developers
- Embedded AI engineers
- Edge system integrators and AI deployment specialists
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
