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Duration 21 hours
Course Outline
Introduction to Cambricon and MLU Architecture
- Insight into Cambricon’s AI chip portfolio
- Understanding MLU architecture and the instruction pipeline
- Review of supported model types and applicable use cases
Setting Up the Development Toolchain
- Installation of BANGPy and the Neuware SDK
- Configuring environments for Python and C++
- Assessing model compatibility and preprocessing requirements
Model Development using BANGPy
- Managing tensor structures and shapes
- Constructing computation graphs
- Implementing custom operations within BANGPy
Deployment with Neuware Runtime
- Converting and loading models for execution
- Controlling execution and inference processes
- Best practices for edge and data center deployment
Performance Optimization
- Optimizing memory mapping and layer 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 scenarios
- Implementing hybrid CPU-MLU inference
End-to-End Project and Practical Use Case
- Lab session: Deploying a vision or NLP model
- Executing edge inference with BANGPy integration
- Evaluating accuracy and throughput
Conclusion and Recommended Next Steps
Requirements
- A solid grasp of machine learning model architectures
- Proficiency in Python and/or C++
- Basic knowledge of model deployment and acceleration techniques
Target Audience
- Embedded AI developers
- ML engineers focused on edge or data center deployment
- Professionals working with AI infrastructure in the region
Testimonials (1)
That we can cover advance topic and work with real-life example
