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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 environments for Python and C++
- Managing model compatibility and preprocessing
Model Development using BANGPy
- Tensor structure and shape management techniques
- Constructing computation graphs
- Implementing custom operation support in BANGPy
Deployment via Neuware Runtime
- Model conversion and loading procedures
- Controlling execution and inference
- Best practices for edge and data center deployment
Optimizing Performance
- Memory mapping and layer tuning strategies
- Execution tracing and profiling methods
- Identifying and resolving common bottlenecks
Integrating MLU into Applications
- Leveraging Neuware APIs for seamless application integration
- Supporting streaming and multi-model scenarios
- Handling hybrid CPU-MLU inference workflows
End-to-End Project and Use Case
- Lab session: Deploying a vision or NLP model
- Implementing edge inference with BANGPy integration
- Evaluating accuracy and throughput
Summary and Future Directions
Requirements
- Proficiency in understanding machine learning model structures
- Practical experience with Python and/or C++
- Familiarity with concepts related to model deployment and acceleration
Target Audience
- Embedded AI developers
- ML engineers focusing on edge or data center deployments
- Developers working within the Chinese AI infrastructure ecosystem
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
