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Course Outline

Overview of CANN Optimization Capabilities

  • An examination of how inference performance is managed within CANN.
  • Optimization objectives for edge and embedded AI systems.
  • Understanding AI Core utilization and memory allocation strategies.

Leveraging the Graph Engine for Analysis

  • Introduction to the Graph Engine and its execution pipeline.
  • Visualizing operator graphs and runtime metrics.
  • Modifying computational graphs to drive optimization.

Profiling Tools and Performance Metrics

  • Utilizing the CANN Profiling Tool (profiler) for workload analysis.
  • Analyzing kernel execution time and identifying bottlenecks.
  • Conducting memory access profiling and applying tiling strategies.

Custom Operator Development with TIK

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

Advanced Operator Optimization with TVM

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

Memory Optimization Techniques

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

Real-World Deployment and Case Studies

  • Case study: Performance tuning for a smart city camera pipeline.
  • Case study: Optimizing the inference stack for autonomous vehicles.
  • Guidelines for iterative profiling and continuous improvement.

Summary and Next Steps

Requirements

  • A robust understanding of deep learning model architectures and training workflows.
  • Practical experience in deploying models using CANN, TensorFlow, or PyTorch.
  • Familiarity with Linux CLI, shell scripting, and Python programming.

Target Audience

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

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  • Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
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Price per private group, online live training, starting from 3200 € + VAT*

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