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Duration 21 hours
Course Outline
Introduction to Cambricon and MLU Architecture
- Overview of Cambricon’s AI chip portfolio
- MLU architecture and instruction pipeline
- Supported model types and applicable use cases
Setting Up the Development Toolchain
- Installation of BANGPy and Neuware SDK
- Configuring environments for Python and C++
- Model compatibility checks and preprocessing
Model Development with BANGPy
- Tensor structure and shape management
- Constructing computation graphs
- Supporting custom operations within BANGPy
Deployment via Neuware Runtime
- Model conversion and loading procedures
- Execution and inference control
- Best practices for edge and data center deployment
Performance Optimization
- Memory mapping and layer tuning
- Execution tracing and profiling
- Identifying common bottlenecks and implementing fixes
Integrating MLU into Applications
- Leveraging Neuware APIs for application integration
- Support for streaming and multi-model scenarios
- Hybrid CPU-MLU inference strategies
End-to-End Project and Use Case
- Lab: Deploying a vision or NLP model
- Edge inference using BANGPy integration
- Testing accuracy and throughput
Summary and Next Steps
Requirements
- Understanding of machine learning model architectures
- Proficiency in Python and/or C++
- Familiarity with model deployment and acceleration principles
Audience
- Embedded AI developers
- ML engineers deploying solutions to edge or datacenter environments
- Developers working within Chinese AI infrastructure ecosystems
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 3900 € + VAT*
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