Cambricon MLU Development with BANGPy and Neuware Training Course
Cambricon MLUs (Machine Learning Units) are specialized AI chips designed for optimized inference and training in both edge and data center environments.
This instructor-led live training, available online or on-site, targets intermediate developers looking to build and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.
Upon completing this training, participants will be able to:
- Set up and configure the development environments for BANGPy and Neuware.
- Develop and optimize Python and C++ based models for Cambricon MLUs.
- Deploy models to edge and data center devices running the Neuware runtime.
- Integrate machine learning workflows with MLU-specific acceleration capabilities.
Course Format
- Interactive lectures and discussions.
- Hands-on practice with BANGPy and Neuware for development and deployment.
- Guided exercises focusing on optimization, integration, and testing.
Customization Options
- For customized training tailored to your specific Cambricon device model or use case, please contact us to arrange a session.
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 Neuware SDK
- Setting up environments for Python and C++
- Model compatibility and preprocessing
Model Development with BANGPy
- Tensor structure and shape management
- Computation graph construction
- Custom operation support in BANGPy
Deploying with Neuware Runtime
- Converting and loading models
- Execution and inference control
- Best practices for edge and data center deployment
Performance Optimization
- Memory mapping and layer tuning
- Execution tracing and profiling
- Common bottlenecks and solutions
Integrating MLU into Applications
- Using 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
- Understanding of machine learning model structures
- Experience with Python and/or C++
- Familiarity with concepts of model deployment and acceleration
Audience
- Embedded AI developers
- ML engineers deploying to edge or data center environments
- Developers working with Chinese AI infrastructure
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Cambricon MLU Development with BANGPy and Neuware Training Course - Enquiry
Testimonials (1)
That we can cover advance topic and work with real-life example
Ruben Khachaturyan - iris-GmbH infrared & intelligent sensors
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