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Duration 14 hours
Course Outline
Introduction to Edge AI and the Ascend 310
- Overview of Edge AI: current trends, limitations, and key applications.
- 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
- Exporting trained models from TensorFlow, PyTorch, and MindSpore.
- Utilizing ATC to convert models into OM format for Ascend devices.
- Addressing unsupported operations and implementing lightweight conversion techniques.
Building Inference Pipelines with AscendCL
- Leveraging the AscendCL API to execute OM models on the Ascend 310.
- Managing input/output preprocessing, memory allocation, and device control.
- Deploying solutions within embedded containers or lightweight runtime environments.
Optimizing for Edge Limitations
- Reducing model size and adjusting precision levels (FP16, INT8).
- Identifying performance bottlenecks using the CANN profiler.
- Optimizing memory layout and data streaming for enhanced performance.
Deployment with MindSpore Lite
- Using the MindSpore Lite runtime for mobile and embedded platforms.
- Comparing MindSpore Lite against the raw AscendCL pipeline.
- Packaging inference models for specific device deployment.
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.
- Monitoring and updating models deployed at the edge.
Summary and Future Directions
Requirements
- Hands-on experience with AI model development or deployment workflows.
- Foundational knowledge of embedded systems, Linux, and Python.
- Proficiency 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
