Cambricon MLU Development with BANGPy and Neuware Training Course
Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to enhance performance in both inference and training tasks within edge computing and data center environments.
This instructor-led live training session (conducted either online or at your location) is tailored for intermediate developers looking to create and deploy AI models using the BANGPy framework alongside Neuware SDK on Cambricon MLU hardware.
Upon completion of this course, participants will be able to:
- Establish and configure development environments for BANGPy and Neuware.
- Create and refine Python- and C++-based models specifically for Cambricon MLUs.
- Deploy these models onto edge devices and data centers that utilize Neuware runtime.
- Incorporate ML workflows with features optimized for MLU acceleration.
Course Format
- Engaging lectures combined with interactive discussions.
- Practical hands-on experience using BANGPy and Neuware for both development and deployment tasks.
- Guided exercises centered on optimization, integration, and testing processes.
Customization Options
- If you require a customized training session based on your specific Cambricon device model or use case, please reach out to us for further 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 use cases
Installing the Development Toolchain
- Installing BANGPy and 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 in BANGPy
Deploying with Neuware Runtime
- Converting and loading models
- Execution and inference control
- Edge and data center deployment practices
Performance Optimization
- Memory mapping and layer tuning
- Execution tracing and profiling
- Common bottlenecks and fixes
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
- An understanding of machine learning model structures
- Experience with Python and/or C++
- Familiarity with model deployment and acceleration concepts
Audience
- Embedded AI developers
- ML engineers deploying to edge or datacenter
- Developers working with Chinese AI infrastructure
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