Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Cambricon and MLU Architecture
- Overview of Cambricon’s AI chip portfolio
- MLU architecture and instruction pipeline
- Supported model types and use cases
Installing the Development Toolchain
- Installing BANGPy and the Neuware SDK
- Environment setup for Python and C++
- Model compatibility and preprocessing
Model Development with BANGPy
- Tensor structure and shape management
- Computation graph construction
- Custom operation support within BANGPy
Deploying with Neuware Runtime
- Converting and loading models
- Execution and inference control
- Best practices for edge and data centre deployment
Performance Optimisation
- Memory mapping and layer tuning
- Execution tracing and profiling
- Addressing common bottlenecks and fixes
Integrating MLU into Applications
- Utilising Neuware APIs for application integration
- Streaming and multi-model support
- Hybrid CPU-MLU inference scenarios
End-to-End Project and Use Case
- Lab: Deploying a vision or NLP model
- Edge inference with BANGPy integration
- Testing accuracy and throughput
Summary and Next Steps
Requirements
- A solid understanding of machine learning model structures
- Practical experience with Python and/or C++
- Familiarity with concepts of model deployment and acceleration
Audience
- Embedded AI developers
- Machine learning engineers deploying to edge or data centre environments
- Developers working with Chinese AI infrastructure
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
