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Course Outline
Introduction to Cambricon and MLU Architecture
- Overview of Cambricon's AI chip portfolio
- MLU architecture and instruction pipeline details
- Supported model types and applicable use cases
Setting Up the Development Toolchain
- Installation of BANGPy and Neuware SDK
- Configuring the environment for Python and C++
- Ensuring model compatibility and preprocessing
Model Development with BANGPy
- Managing tensor structures and shapes
- Constructing computation graphs
- Implementing 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 techniques
- Identifying and resolving common bottlenecks
Integrating MLU into Applications
- Leveraging Neuware APIs for application integration
- Supporting streaming and multi-model scenarios
- Managing hybrid CPU-MLU inference workflows
End-to-End Project and Use Case
- Lab exercise: Deploying a vision or NLP model
- Edge inference implementation with BANGPy integration
- Testing accuracy and throughput performance
Summary and Next Steps
Requirements
- A solid grasp of machine learning model architectures
- Proficiency in Python and/or C++
- Knowledge of model deployment and acceleration principles
Target Audience
- Embedded AI developers
- ML engineers focused on edge or data center deployments
- Developers engaged with Chinese AI infrastructure
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
