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Duration 14 hours
Course Outline
Introduction to Edge AI and the Ascend 310
- Edge AI landscape: current trends, limitations, and use cases
- Architecture of the Huawei Ascend 310 chip and its compatible 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 transform models into OM format for Ascend hardware
- Strategies for managing unsupported operations and lightweight conversions
Developing Inference Pipelines with AscendCL
- Executing OM models on the Ascend 310 via the AscendCL API
- Managing input/output preprocessing, memory allocation, and device control
- Deployment within embedded containers or streamlined runtime environments
Optimization for Edge Limitations
- Techniques for reducing model size and adjusting precision (FP16, INT8)
- Identifying performance bottlenecks using the CANN profiler
- Optimizing memory layout and data streaming for improved throughput
Deploying with MindSpore Lite
- Leveraging the MindSpore Lite runtime for mobile and embedded platforms
- Comparative analysis of MindSpore Lite versus raw AscendCL pipelines
- Packaging inference models for targeted device deployment
Edge Deployment Scenarios and Case Studies
- Case study: Implementing an object detection model on a smart camera with Ascend 310
- Case study: Achieving real-time classification in an IoT sensor hub
- Strategies for monitoring and updating models deployed at the edge
Summary and Future Steps
Requirements
- Practical experience in AI model development or deployment processes
- Fundamental understanding of embedded systems, Linux, and Python
- Proficiency with deep learning frameworks like TensorFlow or PyTorch
Target Audience
- Developers of IoT solutions
- Embedded AI engineers
- Edge system integrators and AI deployment specialists
Testimonials (1)
That we can cover advance topic and work with real-life example
