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Course Outline
Introduction to Edge AI and the Ascend 310
- Understanding Edge AI: current trends, resource constraints, and key applications
- Architecture of the Huawei Ascend 310 chip and its supported toolchain
- The role of CANN within the broader 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 compatibility
- Strategies for handling unsupported operators and achieving lightweight conversions
Building Inference Pipelines with AscendCL
- Executing OM models on Ascend 310 via the AscendCL API
- Managing input/output preprocessing, memory allocation, and device control
- Integration within embedded containers or lightweight runtime environments
Optimization for Edge Constraints
- Reducing model footprint and tuning precision (FP16, INT8)
- Employing the CANN profiler to detect and address performance bottlenecks
- Optimizing memory layout and data streaming for peak performance
Deployment via MindSpore Lite
- Utilizing the MindSpore Lite runtime for mobile and embedded targets
- Evaluating MindSpore Lite against raw AscendCL pipelines
- Packaging inference models for device-specific deployment
Edge Deployment Scenarios and Case Studies
- Case study: Implementing object detection on a smart camera with Ascend 310
- Case study: Achieving real-time classification within an IoT sensor hub
- Strategies for monitoring and updating models deployed at the edge
Summary and Future Directions
Requirements
- Prior experience in AI model development or deployment processes
- Foundational understanding of embedded systems, Linux, and Python
- Working knowledge of deep learning frameworks like TensorFlow or PyTorch
Target Audience
- IoT solution architects
- Embedded AI engineers
- Edge system integrators and AI deployment experts
14 Hours
Testimonials (1)
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