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Duration 14 hours
Course Outline
Overview of CANN Optimization Capabilities
- How inference performance is managed within CANN
- Optimization objectives for edge and embedded AI systems
- Comprehending 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 to achieve optimisation
Profiling Tools and Performance Metrics
- Applying the CANN Profiling Tool (profiler) for workload analysis
- Analysing kernel execution times and identifying bottlenecks
- Memory access profiling and implementing tiling strategies
Custom Operator Development with TIK
- Overview of TIK and its operator programming model
- Implementing custom operators using the TIK DSL
- Testing and benchmarking operator performance
Advanced Operator Optimization 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 Optimization Techniques
- Managing memory layout and buffer placement
- Techniques for reducing on-chip memory consumption
- Best practices for asynchronous execution and resource reuse
Real-World Deployment and Case Studies
- Case study: Performance tuning for smart city camera pipelines
- Case study: Optimising the inference stack for autonomous vehicles
- Guidelines for iterative profiling and continuous improvement
Summary and Next Steps
Requirements
- A solid grasp of deep learning model architectures and training workflows
- Practical experience in deploying models using CANN, TensorFlow, or PyTorch
- Proficiency in Linux CLI, shell scripting, and Python programming
Target Audience
- AI performance engineers
- Specialists in inference optimisation
- Developers working with edge AI or real-time systems
