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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
- Installing BANGPy and Neuware SDK.
- Setting up environments for Python and C++.
- Ensuring model compatibility and preprocessing.
Model Development with BANGPy
- Managing tensor structures and shapes.
- Constructing computation graphs.
- Supporting 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.
- Implementing hybrid CPU-MLU inference scenarios.
End-to-End Project and Use Case
- Lab: Deploying a vision or NLP model.
- Performing edge inference with BANGPy integration.
- Testing for accuracy and throughput.
Summary and Next Steps
Requirements
- A solid understanding of machine learning model structures.
- Experience with Python and/or C++.
- Familiarity with concepts related to model deployment and acceleration.
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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