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Course Outline
Overview of CANN Optimisation Capabilities
- How inference performance is managed within CANN
- Optimisation goals for edge and embedded AI systems
- Understanding AI Core utilisation and memory allocation
Using Graph Engine for Analysis
- Introduction to the Graph Engine and execution pipeline
- Visualising operator graphs and runtime metrics
- Modifying computational graphs for optimisation
Profiling Tools and Performance Metrics
- Using the CANN Profiling Tool (profiler) for workload analysis
- Analysing kernel execution time and 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
- 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 a smart city camera pipeline
- Case study: optimising the autonomous vehicle inference stack
- Guidelines for iterative profiling and continuous improvement
Summary and Next Steps
Requirements
- A strong understanding of deep learning model architectures and training workflows
- Experience with model deployment using CANN, TensorFlow, or PyTorch
- Familiarity with the Linux CLI, shell scripting, and Python programming
Audience
- AI performance engineers
- Inference optimisation specialists
- Developers working with edge AI or real-time systems
14 Hours
