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Course Outline
Introduction to Cambricon and MLU Architecture
- Review of Cambricon’s AI chip ecosystem
- Understanding MLU architecture and instruction pipelines
- Identifying supported model types and applicable use cases
Setting Up the Development Toolchain
- Installation of BANGPy and the Neuware SDK
- Configuring environments for Python and C++ development
- Ensuring model compatibility and preprocessing
Building Models with BANGPy
- Managing tensor structures and shapes
- Constructing computation graphs
- Implementing custom operations within BANGPy
Deployment via Neuware Runtime
- Converting and loading models for execution
- Controlling execution and inference processes
- Best practices for edge and data center deployment
Optimizing Performance
- Tuning memory mapping and layers
- Utilizing execution tracing and profiling tools
- Identifying and resolving common bottlenecks
Integrating MLUs into Applications
- Leveraging Neuware APIs for seamless application integration
- Supporting streaming and multi-model scenarios
- Handling hybrid CPU-MLU inference workflows
End-to-End Project and Case Study
- Practical lab: Deploying a vision or NLP model
- Implementing edge inference with BANGPy integration
- Validating accuracy and throughput performance
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning model structures
- Proficiency in Python and/or C++
- Knowledge of model deployment and acceleration principles
Target Audience
- Embedded AI developers
- ML engineers focused on edge or datacenter deployments
- Developers utilizing Chinese AI infrastructure
21 Hours
Testimonials (1)
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