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Course Outline
Introduction to Cambricon and MLU Architecture
- Overview of Cambricon’s portfolio of AI chips
- MLU architecture and instruction pipeline
- Supported model types and use cases
Installing the Development Toolchain
- Installation of BANGPy and Neuware SDK
- Environment setup 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
- 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
- Utilizing Neuware APIs for seamless application integration
- Support for streaming and multi-model scenarios
- Hybrid CPU-MLU inference architectures
End-to-End Project and Use Case
- Lab: Deploying a vision or NLP model
- Edge inference utilizing BANGPy integration
- Evaluating accuracy and throughput
Summary and Next Steps
Requirements
- Understanding of machine learning model structures
- Proficiency in Python and/or C++
- Familiarity with concepts related to 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
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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