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Course Outline
Introduction to Edge AI and the Ascend 310
- Overview of Edge AI: trends, constraints, and applications
- Huawei Ascend 310 chip architecture and supported toolchain
- Understanding the role of CANN within the edge AI deployment stack
Model Preparation and Conversion
- Exporting trained models from TensorFlow, PyTorch, and MindSpore
- Using ATC to convert models to the OM format for Ascend devices
- Handling unsupported operations and applying lightweight conversion strategies
Developing Inference Pipelines with AscendCL
- Using the AscendCL API to execute OM models on the Ascend 310
- 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)
- Utilizing the CANN profiler to identify performance bottlenecks
- Managing memory layout and data streaming for improved performance
Deploying with MindSpore Lite
- Using the MindSpore Lite runtime for mobile and embedded targets
- Comparing MindSpore Lite with a raw AscendCL pipeline
- Packaging inference models for device-specific deployment
Edge Deployment Scenarios and Case Studies
- Case study: smart camera with an object detection model on the Ascend 310
- Case study: real-time classification in an IoT sensor hub
- Monitoring and updating deployed models at the edge
Summary and Next Steps
Requirements
- Experience with AI model development or deployment workflows
- Basic knowledge 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
