Cambricon MLU Development with BANGPy and Neuware Training Course
Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to optimize inference and training for both edge devices and data center environments.
This instructor-led live training, available either online or onsite, targets intermediate developers who want to build and deploy AI models utilizing 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 tailored for Cambricon MLUs.
- Deploy models to edge and data center devices operating on the Neuware runtime.
- Integrate machine learning workflows with acceleration features specific to MLU.
Course Format
- Interactive lectures and discussions.
- Practical, hands-on development and deployment using BANGPy and Neuware.
- Guided exercises emphasizing optimization, integration, and testing.
Customization Options
- To request a tailored training session based on your specific Cambricon device model or use case, please contact us to arrange.
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 center deployment.
Performance Optimization
- Memory mapping and layer tuning.
- Execution tracing and profiling.
- Identifying common bottlenecks and applying fixes.
Integrating MLU into Applications
- Using Neuware APIs for seamless 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 utilizing 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 related to model deployment and acceleration.
Audience
- Embedded AI developers.
- Machine learning 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
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