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Course Outline
Introduction to Edge AI and Ascend 310
- An overview of Edge AI, including current trends, constraints, and applications.
- Details on the Huawei Ascend 310 chip architecture and its supported toolchain.
- The role of CANN within the edge AI deployment stack.
Model Preparation and Conversion
- Techniques for exporting trained models from TensorFlow, PyTorch, and MindSpore.
- Utilizing ATC to convert models into the OM format for Ascend devices.
- Strategies for handling unsupported operations and lightweight conversion.
Developing 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 solutions within embedded containers or lightweight runtime environments.
Optimization for Edge Constraints
- Reducing model size and tuning precision (FP16, INT8).
- Identifying performance bottlenecks using the CANN profiler.
- Optimizing memory layout and data streaming for improved performance.
Deploying with MindSpore Lite
- Using the MindSpore Lite runtime for mobile and embedded targets.
- Comparing MindSpore Lite with raw AscendCL pipelines.
- Packaging inference models for deployment on specific devices.
Edge Deployment Scenarios and Case Studies
- Case study: Implementing a smart camera with an object detection model on the Ascend 310.
- Case study: Real-time classification within an IoT sensor hub.
- Strategies for monitoring and updating deployed models at the edge.
Summary and Next Steps
Requirements
- Prior experience in AI model development or deployment workflows.
- Basic understanding of embedded systems, Linux, and Python.
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch.
Audience
- Developers working on IoT solutions.
- Engineers specializing in embedded AI.
- Edge system integrators and AI deployment specialists.
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
