Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Introduction to Edge AI and Ascend 310
- Overview of Edge AI: emerging trends, constraints, and use cases
- Huawei Ascend 310 chip architecture and its supported toolchain
- Role of CANN within the edge AI deployment stack
Model Preparation and Conversion
- Exporting trained models from TensorFlow, PyTorch, and MindSpore
- Utilizing ATC to convert models into OM format for Ascend devices
- Addressing unsupported operations and applying lightweight conversion strategies
Building Inference Pipelines with AscendCL
- Executing OM models on Ascend 310 using 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 size and adjusting precision (FP16, INT8)
- Identifying performance bottlenecks with the CANN profiler
- Optimizing memory layout and data streaming for enhanced performance
Deployment using MindSpore Lite
- Utilizing the MindSpore Lite runtime for mobile and embedded targets
- Comparing MindSpore Lite against the native AscendCL pipeline
- Packaging inference models for device-specific deployment needs
Edge Deployment Scenarios and Case Studies
- Case study: implementing object detection via smart camera on Ascend 310
- Case study: executing real-time classification in an IoT sensor hub
- Monitoring and updating deployed models 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
- Proficiency 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
