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Duration 14 hours
Course Outline
Introduction to Edge AI and the Ascend 310
- Overview of Edge AI: key trends, constraints, and applications
- Architecture of the Huawei Ascend 310 chip and its supported toolchain
- The role of CANN in the edge AI deployment ecosystem
Model Preparation and Conversion
- Exporting trained models from TensorFlow, PyTorch, and MindSpore
- Utilizing ATC to transform models into OM format for Ascend devices
- Addressing unsupported operations and implementing lightweight conversion strategies
Building Inference Pipelines with AscendCL
- Leveraging the AscendCL API to execute OM models on the Ascend 310
- Managing input/output preprocessing, memory operations, and device control
- Deployment within embedded containers or lightweight runtime environments
Optimization for Edge Constraints
- Reducing model size and adjusting precision (FP16, INT8)
- Identifying performance bottlenecks using the CANN profiler
- Optimizing memory layout and data streaming for enhanced performance
Deployment via MindSpore Lite
- Using the MindSpore Lite runtime for mobile and embedded targets
- Comparing MindSpore Lite against the raw AscendCL pipeline
- Packaging inference models for specific device deployment
Edge Deployment Scenarios and Case Studies
- Case study: implementing an object detection model on a smart camera using Ascend 310
- Case study: achieving real-time classification in an IoT sensor hub
- Monitoring and updating deployed models at the edge
Summary and Next Steps
Requirements
- Practical experience with AI model development or deployment workflows
- Fundamental 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
