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Duration 14 hours
Course Outline
Introduction to Edge AI and the Ascend 310
- Overview of Edge AI: exploring trends, constraints, and key applications.
- Detailed look at the Huawei Ascend 310 chip architecture and its supported toolchain.
- Understanding the role of CANN within the broader 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 employing lightweight conversion strategies.
Building Inference Pipelines with AscendCL
- Leveraging the AscendCL API to execute OM models on the Ascend 310.
- Managing input/output preprocessing, memory handling, and device control.
- Deploying within embedded containers or lightweight runtime environments.
Optimization for Edge Constraints
- Reducing model size and applying precision tuning (FP16, INT8).
- Identifying performance bottlenecks using the CANN profiler.
- Optimizing memory layout and data streaming to enhance performance.
Deployment with MindSpore Lite
- Utilizing the MindSpore Lite runtime for mobile and embedded targets.
- Comparing MindSpore Lite against raw AscendCL pipelines.
- Packaging inference models for specific device deployment.
Edge Deployment Scenarios and Case Studies
- Case study: Implementing a smart camera with an object detection model on the 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 Next Steps
Requirements
- Practical experience in 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
