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Duration 21 hours
Course Outline
Foundations of Cambricon and MLU Architecture
- An overview of Cambricon’s AI chip offerings
- MLU design and instruction flow
- Compatible model types and applicable use cases
Setting Up the Development Toolchain
- Installation of BANGPy and the Neuware SDK
- Configuring environments for Python and C++
- Model compatibility checks and preprocessing
Building Models with BANGPy
- Managing tensor structures and shapes
- Constructing computation graphs
- Supporting custom operations within BANGPy
Releasing with Neuware Runtime
- Model conversion and loading procedures
- Controlling execution and inference
- Best practices for edge and data center releases
Enhancing Performance
- Tuning memory mapping and layers
- Tracing execution and profiling
- Identifying and resolving common bottlenecks
Incorporating MLU into Applications
- Leveraging Neuware APIs for seamless integration
- Supporting streaming and multi-model workloads
- Handling hybrid CPU-MLU inference scenarios
End-to-End Project and Application Case
- Practical exercise: Releasing a vision or NLP model
- Edge inference leveraging BANGPy
- Evaluating accuracy and throughput
Wrap-up and Future Directions
Requirements
- A solid grasp of machine learning model architectures
- Proficiency in Python and/or C++
- A familiarity with concepts related to model deployment and acceleration
Target Audience
- Embedded AI developers
- ML engineers focused on edge or data center deployments
- Developers engaging with Chinese AI infrastructure
Testimonials (1)
That we can cover advance topic and work with real-life example
