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Duration 21 hours
Course Outline
Introduction to Cambricon and MLU Architecture
- An overview of Cambricon’s AI chip portfolio
- MLU architecture and instruction pipeline details
- Supported model types and applicable use cases
Setting Up the Development Toolchain
- Installation of BANGPy and the Neuware SDK
- Configuring the environment for Python and C++
- Handling model compatibility and preprocessing
Model Development with BANGPy
- Managing tensor structures and shapes
- Constructing computation graphs
- Implementing custom operation support in BANGPy
Deployment with Neuware Runtime
- Converting and loading models
- Controlling execution and inference
- Best practices for edge and data center deployment
Performance Optimization
- Memory mapping and layer tuning
- Execution tracing and profiling
- Identifying and resolving common bottlenecks
Integrating MLU into Applications
- Leveraging Neuware APIs for application integration
- Support for streaming and multi-model scenarios
- Hybrid CPU-MLU inference setups
End-to-End Project and Use Case
- Lab: Deploying a vision or NLP model
- Edge inference integrated with BANGPy
- Evaluating accuracy and throughput
Conclusion and Next Steps
Requirements
- A solid grasp of machine learning model architectures
- Proficiency in Python and/or C++
- Knowledge of model deployment and acceleration principles
Target Audience
- Embedded AI developers
- ML engineers focused on edge or data center deployment
- Developers working within Chinese AI infrastructure ecosystems
Testimonials (1)
That we can cover advance topic and work with real-life example
