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Duration 14 hours
Course Outline
Introduction to Edge AI and Ascend 310
- An overview of Edge AI: key trends, constraints, and applications
- The architecture of the Huawei Ascend 310 chip and its supported toolchain
- The role of CANN within the edge AI deployment stack
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
Developing Inference Pipelines with AscendCL
- Executing OM models on the Ascend 310 using the AscendCL API
- Managing input/output preprocessing, memory allocation, and device control
- Deploying solutions 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 to enhance performance
Deploying with MindSpore Lite
- Leveraging the MindSpore Lite runtime for mobile and embedded targets
- Comparing MindSpore Lite against raw AscendCL pipelines
- Packaging inference models for deployment on specific devices
Edge Deployment Scenarios and Case Studies
- Case study: Implementing object detection on a smart camera using the Ascend 310
- Case study: Real-time classification within an IoT sensor hub
- Strategies for monitoring and updating models deployed at the edge
Summary and Next Steps
Requirements
- Prior experience with AI model development or deployment workflows
- Foundational 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
Testimonials (1)
That we can cover advance topic and work with real-life example
