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Course Outline
Fundamentals of Cambricon and MLU Architecture
- Introduction to Cambricon’s AI chip ecosystem
- Deep dive into MLU architecture and instruction pipelines
- Review of supported model types and applicable use cases
Setting Up the Development Toolchain
- Installation of BANGPy and the Neuware SDK
- Configuring environments for Python and C++ development
- Handling model compatibility and preprocessing requirements
Building Models with BANGPy
- Managing tensor structures and shapes
- Constructing computation graphs
- Implementing custom operations within BANGPy
Deployment via Neuware Runtime
- Model conversion and loading procedures
- Controlling execution and inference
- Best practices for edge and data center deployment
Optimizing Performance
- Strategies for memory mapping and layer tuning
- Utilizing execution tracing and profiling tools
- Identifying and resolving common performance bottlenecks
Application Integration with MLUs
- Leveraging Neuware APIs for seamless application integration
- Supporting streaming and multi-model scenarios
- Implementing hybrid CPU-MLU inference workflows
End-to-End Project and Practical Application
- Hands-on lab: Deploying a vision or NLP model
- Edge inference implementation with BANGPy integration
- Evaluating accuracy and throughput performance
Wrap-Up and Future Directions
Requirements
- A solid grasp of machine learning model architectures
- Proficiency in Python and/or C++
- Knowledge of model deployment strategies and acceleration principles
Target Audience
- Embedded AI developers
- ML engineers focused on edge or datacenter deployments
- Professionals working within Chinese AI infrastructure ecosystems
21 Hours
Testimonials (1)
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