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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 use cases.
Installing the Development Toolchain
- Installation of BANGPy and Neuware SDK.
- Setting up environments 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 in BANGPy.
Deploying with Neuware Runtime
- Converting and loading models.
- Controlling execution and inference.
- Best practices for edge and data center deployment.
Performance Optimization
- Memory mapping and layer tuning.
- Execution tracing and profiling.
- Identifying and resolving common bottlenecks.
Integrating MLU into Applications
- Using Neuware APIs for application integration.
- Supporting streaming and multi-model scenarios.
- Hybrid CPU-MLU inference setups.
End-to-End Project and Use Case
- Lab: Deploying a vision or NLP model.
- Implementing edge inference with BANGPy integration.
- Testing for accuracy and throughput.
Summary and Next Steps
Requirements
- Familiarity with the structure of machine learning models.
- Experience using Python and/or C++.
- Understanding of model deployment and acceleration concepts.
Audience
- Embedded AI developers.
- ML engineers targeting edge or data center deployments.
- Developers working with Chinese AI infrastructure.
21 Hours
Testimonials (1)
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