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Duration 14 hours
Course Outline
Introduction to Edge AI and the Ascend 310
- Edge AI landscape: trends, limitations, and key applications
- Architecture of the Huawei Ascend 310 chip and its supported toolchain
- The role of CANN in the edge AI deployment ecosystem
Model Preparation and Conversion
- Exporting trained models from TensorFlow, PyTorch, and MindSpore
- Leveraging ATC to transform models into OM format for Ascend hardware
- Addressing unsupported operations and adopting lightweight conversion tactics
Building Inference Pipelines with AscendCL
- Executing OM models on Ascend 310 through the AscendCL API
- Managing input/output preprocessing, memory allocation, and device control
- Implementation within embedded containers or lightweight runtime systems
Optimization for Edge Constraints
- Minimizing model size and adjusting precision (FP16, INT8)
- Utilizing the CANN profiler to detect performance bottlenecks
- Optimizing memory layout and data streams for enhanced performance
Deployment using MindSpore Lite
- Employing the MindSpore Lite runtime for mobile and embedded targets
- Evaluating MindSpore Lite against raw AscendCL pipelines
- Packaging inference models for device-specific deployment
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
- Monitoring and updating models deployed at the edge
Summary and Next Steps
Requirements
- Proficiency in AI model development or deployment processes
- Foundational understanding of embedded systems, Linux, and Python
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch
Target Audience
- IoT solution architects and 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
