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Course Outline
Introduction to Cambricon and MLU Architecture
- Overview of Cambricon’s AI chip lineup
- MLU architecture and instruction pipeline
- Supported model types and applicable use cases
Setting Up the Development Toolchain
- Installation of BANGPy and Neuware SDK
- Configuring environments for Python and C++
- Model compatibility and preprocessing
Model Development with BANGPy
- Tensor structure and shape management
- Construction of computation graphs
- Support for custom operations in BANGPy
Deployment via Neuware Runtime
- Model conversion and loading procedures
- Control of execution and inference
- Best practices for edge and data centre deployment
Performance Optimization
- Memory mapping and layer tuning
- Execution tracing and profiling
- Identifying common bottlenecks and solutions
Integrating MLU into Applications
- Leveraging Neuware APIs for application integration
- Support for streaming and multi-model scenarios
- Hybrid CPU-MLU inference implementations
End-to-End Project and Use Case
- Lab: Deploying a vision or NLP model
- Edge inference with BANGPy integration
- Evaluation of accuracy and throughput
Conclusion and Future Directions
Requirements
- A foundational grasp of machine learning model architectures
- Proficiency in Python and/or C++
- Awareness of model deployment and acceleration principles
Target Audience
- Embedded AI developers
- ML engineers deploying solutions to edge or data centre environments
- Developers utilising Chinese AI infrastructure
21 Hours
Testimonials (1)
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