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Course Outline
Introduction to Cambricon and MLU Architecture
- Survey of Cambricon's AI chip ecosystem
- MLU architectural design and instruction pipelines
- Supported model categories and real-world applications
Setting Up the Development Toolchain
- Installation of BANGPy and Neuware SDK
- Configuring environments for Python and C++ development
- Ensuring model compatibility and pre-processing workflows
Model Development using BANGPy
- Managing tensor structures and shapes
- Building computation graphs
- Implementing custom operations within BANGPy
Deployment via Neuware Runtime
- Model conversion and loading procedures
- Controlling execution and inference processes
- Best practices for edge and data center deployment
Optimizing Performance
- Memory mapping strategies and layer-level tuning
- Utilizing execution tracing and profiling tools
- Identifying and resolving common performance bottlenecks
Integrating MLUs into Applications
- Leveraging Neuware APIs for seamless application integration
- Supporting streaming and multi-model setups
- Implementing hybrid CPU-MLU inference scenarios
End-to-End Project and Practical Use Case
- Practical lab: Deploying a vision or NLP model
- Edge inference implementation with BANGPy integration
- Validating model accuracy and throughput
Wrap-up and Future Directions
Requirements
- A solid grasp of machine learning model architectures
- Proficiency in Python and/or C++
- Knowledge of model deployment and acceleration principles
Intended Audience
- Embedded AI developers
- ML engineers responsible for edge or datacenter deployments
- Developers utilizing Chinese AI infrastructure
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
