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Course Outline
Introduction to Cambricon and MLU Architecture
- Overview of Cambricon’s AI chip portfolio.
- MLU architecture and instruction pipeline.
- Supported model types and applicable use cases.
Installing the Development Toolchain
- Installation of BANGPy and Neuware SDK.
- Environment setup for Python and C++.
- Model compatibility and preprocessing techniques.
Model Development with BANGPy
- Tensor structure and shape management.
- Construction of computation graphs.
- Support for custom operations within BANGPy.
Deploying with Neuware Runtime
- Converting and loading models.
- Managing execution and inference control.
- Best practices for edge and data center deployment.
Performance Optimization
- Memory mapping and layer tuning.
- Execution tracing and profiling.
- Identifying common bottlenecks and implementing fixes.
Integrating MLU into Applications
- Utilizing Neuware APIs for application integration.
- Support for streaming and multi-model scenarios.
- Hybrid inference scenarios involving CPUs and MLUs.
End-to-End Project and Use Case
- Lab: Deploying a vision or NLP model.
- Edge inference utilizing BANGPy integration.
- Testing for accuracy and throughput.
Summary and Next Steps
Requirements
- A solid understanding of machine learning model structures.
- Proficiency in Python and/or C++.
- Familiarity with concepts related to model deployment and acceleration.
Target Audience
- Embedded AI developers.
- Machine learning engineers deploying solutions to edge or data center environments.
- Developers working with Chinese AI infrastructure.
21 Hours
Testimonials (1)
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