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
 21 Hours

Delivery Options

Private Group Training

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  • Pre-course call with your trainer
  • Customisation of the learning experience to achieve your goals -
    • Bespoke outlines
    • Practical hands-on exercises containing data / scenarios recognisable to the learners
  • Training scheduled on a date of your choice
  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.

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