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Course Outline
Introduction to Cambricon and MLU Architecture
- Overview of Cambricon’s portfolio of AI chips
- Details on MLU architecture and instruction pipeline
- Supported model types and potential use cases
Setting Up the Development Toolchain
- Installation of BANGPy and Neuware SDK
- Configuring environments for Python and C++
- Managing model compatibility and preprocessing
Developing Models with BANGPy
- Managing tensor structures and shapes
- Constructing computation graphs
- Support for custom operations within BANGPy
Deployment via Neuware Runtime
- Converting and loading models
- Controlling execution and inference
- Best practices for deploying to edge and data centers
Performance Optimization
- Tuning layers and memory mapping
- Profiling and execution tracing
- Identifying and resolving common bottlenecks
Integrating MLU into Applications
- Utilizing Neuware APIs for application integration
- Supporting streaming and multi-model scenarios
- Implementing hybrid CPU-MLU inference setups
End-to-End Project and Use Case
- Lab exercise: Deploying a vision or NLP model
- Performing edge inference with BANGPy integration
- Evaluating accuracy and throughput
Summary and Next Steps
Requirements
- A solid grasp of machine learning model structures
- Practical experience with Python and/or C++
- Familiarity with the concepts of model deployment and acceleration
Target Audience
- Developers specializing in embedded AI
- ML engineers focusing on deployment to edge or data centers
- Developers working within Chinese AI infrastructure
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
