Cambricon MLU Development with BANGPy and Neuware Training Course
Cambricon MLUs (Machine Learning Units) are purpose-built AI chips engineered for optimal inference and training performance in both edge computing and data centre environments.
This instructor-led, live training session (available online or at your premises) is designed for intermediate developers who want to build and deploy AI models leveraging the BANGPy framework and Neuware SDK on Cambricon MLU hardware.
Upon completing this training, participants will be equipped to:
- Configure and set up the BANGPy and Neuware development environments.
- Develop and optimise Python- and C++-based models tailored for Cambricon MLUs.
- Deploy models onto edge and data centre devices operating with the Neuware runtime.
- Integrate machine learning workflows with hardware-specific acceleration capabilities.
Course Format
- Interactive lectures and discussions.
- Practical, hands-on experience using BANGPy and Neuware for development and deployment.
- Guided exercises concentrating on optimization, integration, and testing.
Customisation Options
- For a training programme tailored to your specific Cambricon device model or use case, please contact us to make arrangements.
Course Outline
Introduction to Cambricon and MLU Architecture
- Overview of Cambricon’s AI chip portfolio
- MLU architecture and instruction pipeline
- Supported model types and applicable use cases
Installing the Development Toolchain
- Installation of BANGPy and Neuware SDK
- Environment configuration for Python and C++
- Model compatibility checks and preprocessing steps
Model Development with BANGPy
- Tensor structure and shape management
- Construction of computation graphs
- Support for custom operations within BANGPy
Deploying with Neuware Runtime
- Model conversion and loading processes
- Execution and inference control mechanisms
- Best practices for edge and data centre deployment
Performance Optimization
- Memory mapping and layer tuning
- Execution tracing and profiling techniques
- Identifying and resolving common bottlenecks
Integrating MLU into Applications
- Utilising Neuware APIs for seamless application integration
- Streaming capabilities 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 implementation with BANGPy integration
- Testing for accuracy and throughput
Summary and Next Steps
Requirements
- A solid understanding of machine learning model architectures
- Practical experience with Python and/or C++
- Familiarity with concepts related to model deployment and acceleration
Target Audience
- Embedded AI developers
- ML engineers deploying solutions to edge or data centre 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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