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Duration 21 hours
Course Outline
Overview of Cambricon and MLU Architecture
- Introduction to Cambricon’s AI chip portfolio
- MLU architectural design and instruction pipeline
- Compatible model types and applicable use cases
Setting Up the Development Toolchain
- Installing the BANGPy and Neuware SDK
- Configuring environments for Python and C++
- Managing model compatibility and preprocessing steps
Model Development Using BANGPy
- Understanding tensor structures and shape management
- Constructing computation graphs
- Implementing custom operations within BANGPy
Deployment via Neuware Runtime
- Model conversion and loading procedures
- Controlling execution and inference
- Best practices for edge and data center deployment
Optimizing Performance
- Tuning memory mapping and layer configurations
- 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 scenarios
- Handling hybrid CPU-MLU inference workflows
End-to-End Project and Real-World Use Cases
- Hands-on lab: Deploying a vision or NLP model
- Implementing edge inference with BANGPy
- Evaluating model accuracy and throughput
Summary and Future Pathways
Requirements
- A solid grasp of machine learning model architectures
- Proficiency in Python and/or C++
- Working knowledge of model deployment and acceleration principles
Target Audience
- Embedded AI developers
- ML engineers focused on edge or data center deployments
- Developers operating within Chinese AI infrastructure ecosystems
Testimonials (1)
That we can cover advance topic and work with real-life example
