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Duration 14 hours
Course Outline
Introduction to Edge AI and the Ascend 310
- Edge AI overview: key trends, limitations, and use cases
- Architecture of the Huawei Ascend 310 chip and its supported toolchain
- The role of CANN within the edge AI deployment ecosystem
Model Preparation and Conversion Processes
- Exporting trained models from TensorFlow, PyTorch, and MindSpore
- Utilizing ATC to transform models into OM format for Ascend hardware
- Managing unsupported operations and employing lightweight conversion techniques
Creating Inference Pipelines with AscendCL
- Leveraging the AscendCL API to execute OM models on the Ascend 310
- Preprocessing inputs/outputs, memory management, and device control
- Integration within embedded containers or lightweight runtime environments
Optimization for Edge Limitations
- Minimizing model size and adjusting precision (FP16, INT8)
- Identifying performance bottlenecks using the CANN profiler
- Optimizing memory layout and data streaming for improved efficiency
Deployment via MindSpore Lite
- Utilizing the MindSpore Lite runtime for mobile and embedded platforms
- Comparing MindSpore Lite with the native AscendCL pipeline
- Packaging inference models for specific device deployment
Edge Deployment Scenarios and Practical Cases
- Case study: Implementing object detection on a smart camera using Ascend 310
- Case study: Real-time classification in an IoT sensor hub
- Monitoring and updating models deployed at the edge
Recap and Future Directions
Requirements
- Prior experience in AI model development or deployment processes
- Basic understanding of embedded systems, Linux, and Python
- Familiarity with deep learning frameworks like TensorFlow or PyTorch
Target Audience
- IoT solution developers
- Embedded AI engineers
- Edge system integrators and AI deployment specialists
Testimonials (1)
That we can cover advance topic and work with real-life example
