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Duration 21 hours
Course Outline
Understanding Cambricon and MLU Architectures
- Exploration of Cambricon’s AI chip lineup
- Deep dive into MLU architecture and instruction pipelines
- Identification of supported model types and applicable scenarios
Setting Up the Development Toolkit
- Installation of BANGPy and the Neuware SDK
- Configuring the environment for Python and C++
- Ensuring model compatibility and handling preprocessing
Developing Models with BANGPy
- Managing tensor structures and shapes
- Constructing computation graphs
- Implementing custom operations within BANGPy
Deployment via Neuware Runtime
- Processes for converting and loading models
- Controlling execution and inference
- Best practices for edge and data center deployment
Optimizing Performance
- Strategies for memory mapping and layer tuning
- Utilizing execution tracing and profiling tools
- Identifying and resolving common bottlenecks
Integrating MLUs into Applications
- Leveraging Neuware APIs for seamless application integration
- Supporting streaming and multi-model setups
- Handling hybrid CPU-MLU inference scenarios
End-to-End Project Application
- Practical lab: Deploying a vision or NLP model
- Implementing edge inference with BANGPy integration
- Evaluating accuracy and throughput
Wrap-up and Future Steps
Requirements
- A solid grasp of machine learning model architectures
- Proficiency in Python and/or C++
- Knowledge of model deployment strategies and acceleration techniques
Target Audience
- Embedded AI specialists
- ML engineers focusing on edge or datacenter implementations
- Developers working within Chinese AI infrastructure ecosystems
Testimonials (1)
That we can cover advance topic and work with real-life example
