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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 the Neuware SDK
- Configuring the environment for Python and C++
- Managing model compatibility and preprocessing
Model Development using BANGPy
- Understanding tensor structure and shape management
- Constructing computation graphs
- Implementing custom operation support in BANGPy
Deployment via Neuware Runtime
- Model conversion and loading procedures
- Managing execution and inference control
- Best practices for edge and data centre deployment
Performance Optimisation
- Memory mapping and layer tuning strategies
- Execution tracing and profiling techniques
- Identifying and resolving common bottlenecks
Integrating MLU into Applications
- Leveraging Neuware APIs for application-level integration
- Support for streaming and multi-model scenarios
- Implementing hybrid CPU-MLU inference workflows
End-to-End Project and Use Case
- Practical lab: Deploying a vision or NLP model
- Edge inference implementation with BANGPy
- Evaluating accuracy and throughput performance
Summary and Next Steps
Requirements
- A foundational understanding of machine learning model architectures
- Proficiency in Python and/or C++
- Familiarity with concepts related to model deployment and acceleration
Target Audience
- Embedded AI developers
- ML engineers deploying solutions to edge or data centre infrastructure
- Developers working within Chinese AI ecosystems
Testimonials (1)
That we can cover advance topic and work with real-life example
