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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
Installing the Development Toolchain
- Installing the BANGPy and Neuware SDKs
- Setting up the environment for Python and C++
- Model compatibility checks and preprocessing
Model Development with BANGPy
- Managing tensor structures and shapes
- Constructing computation graphs
- Support for custom operations within BANGPy
Deploying with Neuware Runtime
- Model conversion and loading procedures
- Controlling execution and inference
- Best practices for edge and data center deployment
Performance Optimization
- Memory mapping and layer tuning
- Execution tracing and profiling techniques
- Identifying and resolving common bottlenecks
Integrating MLU into Applications
- Leveraging Neuware APIs for seamless application integration
- Streaming capabilities and multi-model support
- Hybrid CPU-MLU inference scenarios
End-to-End Project and Use Case
- Lab exercise: Deploying a vision or NLP model
- Edge inference through BANGPy integration
- Testing for accuracy and throughput
Summary and Next Steps
Requirements
- A solid grasp of machine learning model structures
- Practical experience with Python and/or C++
- Knowledge of model deployment and acceleration principles
Target Audience
- Embedded AI developers
- ML engineers focused on edge or data center deployments
- Developers working with Chinese AI infrastructure
Testimonials (1)
That we can cover advance topic and work with real-life example
