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Course Outline

Overview of CANN Optimisation Capabilities

  • How inference performance is managed within CANN.
  • Optimisation objectives for edge and embedded AI systems.
  • Understanding AI Core utilisation and memory allocation.

Utilising the Graph Engine for Analysis

  • Introduction to the Graph Engine and its execution pipeline.
  • Visualising operator graphs and runtime metrics.
  • Modifying computational graphs for optimisation.

Profiling Tools and Performance Metrics

  • Employing the CANN Profiling Tool (profiler) for workload analysis.
  • Analyzing kernel execution time and identifying bottlenecks.
  • Memory access profiling and tiling strategies.

Custom Operator Development with TIK

  • Overview of TIK and the operator programming model.
  • Implementing a custom operator using TIK DSL.
  • Testing and benchmarking operator performance.

Advanced Operator Optimisation with TVM

  • Introduction to TVM integration with CANN.
  • Auto-tuning strategies for computational graphs.
  • Determining when and how to switch between TVM and TIK.

Memory Optimisation Techniques

  • Managing memory layout and buffer placement.
  • Techniques to reduce on-chip memory consumption.
  • Best practices for asynchronous execution and reuse.

Real-World Deployment and Case Studies

  • Case study: performance tuning for smart city camera pipelines.
  • Case study: optimising autonomous vehicle inference stacks.
  • Guidelines for iterative profiling and continuous improvement.

Summary and Next Steps

Requirements

  • Robust understanding of deep learning model architectures and training workflows.
  • Experience in model deployment using CANN, TensorFlow, or PyTorch.
  • Familiarity with Linux CLI, shell scripting, and Python programming.

Target Audience

  • AI performance engineers.
  • Inference optimisation specialists.
  • Developers working with edge AI or real-time systems.
 14 Hours

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  • Format: Online (live), In-company (at your offices), or Hybrid.
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