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Course Outline
Introduction to Cambricon and MLU Architecture
- Overview of Cambricon’s AI chip portfolio.
- Understanding MLU architecture and the instruction pipeline.
- Supported model types and typical use cases.
Installing the Development Toolchain
- Installation of BANGPy and the Neuware SDK.
- Configuring Python and C++ development environments.
- Managing model compatibility and preprocessing steps.
Model Development with BANGPy
- Managing tensor structures and shapes.
- Constructing computation graphs.
- Implementing custom operation support within BANGPy.
Deploying with Neuware Runtime
- Processes for converting and loading models.
- Controlling execution and inference.
- Best practices for edge and data center deployment.
Performance Optimization
- Tuning memory mapping and layer performance.
- Utilizing execution tracing and profiling tools.
- Identifying and resolving common performance bottlenecks.
Integrating MLU into Applications
- Leveraging Neuware APIs for seamless application integration.
- Supporting streaming and multi-model scenarios.
- Handling hybrid CPU-MLU inference architectures.
End-to-End Project and Use Case
- Lab session: Deploying a vision or NLP model.
- Implementing edge inference with BANGPy integration.
- Validating accuracy and throughput metrics.
Summary and Next Steps
Requirements
- A solid understanding of machine learning model architectures.
- Proficiency in Python and/or C++.
- General knowledge of model deployment and acceleration strategies.
Target Audience
- Embedded AI developers.
- ML engineers focusing on edge or data center deployments.
- Developers working with Chinese AI infrastructure.
21 Hours
Testimonials (1)
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