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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 typical use cases.
Installing the Development Toolchain
- Installation of BANGPy and the Neuware SDK.
- Setting up the environment for Python and C++.
- Model compatibility checks and preprocessing.
Model Development with BANGPy
- Tensor structure and shape management.
- Construction of computation graphs.
- Support for custom operations in BANGPy.
Deploying with Neuware Runtime
- Model conversion and loading.
- Execution and inference control.
- 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
- Utilizing Neuware APIs for application integration.
- Streaming and multi-model support.
- Hybrid CPU-MLU inference scenarios.
End-to-End Project and Use Case
- Lab exercise: Deploying a vision or NLP model.
- Edge inference via BANGPy integration.
- Testing model accuracy and throughput.
Summary and Next Steps
Requirements
- A solid understanding of machine learning model structures.
- Proficiency in Python and/or C++.
- Familiarity with model deployment and acceleration concepts.
Target Audience
- Embedded AI developers.
- ML engineers specializing in edge or data center deployments.
- Developers working within Chinese AI infrastructure ecosystems.
21 Hours
Testimonials (1)
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