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Duration 14 hours
Course Outline
Introduction to Edge AI and the Ascend 310
- Overview of Edge AI: current trends, inherent constraints, and key applications.
- Architecture of the Huawei Ascend 310 chip and its supported toolchain.
- Understanding CANN's role within the edge AI deployment ecosystem.
Model Preparation and Conversion Process
- Exporting trained models from TensorFlow, PyTorch, and MindSpore.
- Utilizing ATC to transform models into OM format for Ascend hardware.
- Strategies for handling unsupported operations and lightweight conversion techniques.
Building Inference Pipelines with AscendCL
- Executing OM models on the Ascend 310 using the AscendCL API.
- Managing input/output preprocessing, memory allocation, and device control.
- Deployment within embedded containers or lightweight runtime environments.
Optimizing for Edge Constraints
- Techniques for reducing model size and precision tuning (FP16, INT8).
- Employing the CANN profiler to pinpoint performance bottlenecks.
- Optimizing memory layout and data streaming to enhance performance.
Deployment via MindSpore Lite
- Utilizing the MindSpore Lite runtime for mobile and embedded platforms.
- Comparing the MindSpore Lite approach with raw AscendCL pipelines.
- Packaging inference models for specific device deployments.
Edge Deployment Scenarios and Case Studies
- Case study: implementing object detection on a smart camera using Ascend 310.
- Case study: real-time classification within an IoT sensor hub.
- Strategies for monitoring and updating deployed edge models.
Summary and Recommended Next Steps
Requirements
- Practical experience in AI model development or deployment processes.
- Fundamental understanding of embedded systems, Linux, and Python.
- Familiarity with major 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
