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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.
  • Best practices for edge and datacenter deployment.

Performance Optimization

  • Memory mapping and layer tuning.
  • Execution tracing and profiling.
  • Common bottlenecks and solutions.

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

  • Understanding of machine learning model structures.
  • Experience with Python and/or C++.
  • Familiarity with concepts of model deployment and acceleration.

Audience

  • Embedded AI developers.
  • ML engineers deploying to edge or datacenter environments.
  • Developers working with Chinese AI infrastructure.
 21 Hours

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