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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++
- Model compatibility 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
- Converting and loading models
- Execution and inference control
- Best practices for edge and data center deployment
Performance Optimization
- Memory mapping and layer tuning
- Execution tracing and profiling
- Addressing common bottlenecks and solutions
Integrating MLU into Applications
- Utilizing Neuware APIs for application integration
- Support for streaming and multi-model scenarios
- Hybrid CPU-MLU inference scenarios
End-to-End Project and Use Case
- Lab: Deploying a vision or NLP model
- Edge inference with BANGPy integration
- Evaluating accuracy and throughput
Summary and Next Steps
Requirements
- Understanding of machine learning model architectures
- Experience with Python and/or C++
- Familiarity with concepts of model deployment and acceleration
Audience
- Embedded AI developers
- ML engineers deploying solutions to edge or data center environments
- Developers working with Chinese AI infrastructure
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
