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Course Outline
Overview of CANN Optimization Capabilities
- Understanding how inference performance is managed within CANN
- Defining optimization goals for edge and embedded AI systems
- Understanding AI Core utilization and memory allocation
Using Graph Engine for Analysis
- Introduction to the Graph Engine and execution pipeline
- Visualizing operator graphs and runtime metrics
- Modifying computational graphs for optimized performance
Profiling Tools and Performance Metrics
- Employing 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 TIK and the 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
- Guidelines for switching between TVM and TIK when appropriate
Memory Optimization Techniques
- Managing memory layout and buffer placement
- Techniques to minimize 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
- Deep understanding of deep learning model architectures and training workflows
- Practical experience deploying models via CANN, TensorFlow, or PyTorch
- Familiarity with Linux CLI, shell scripting, and Python programming
Target Audience
- AI performance engineers
- Specialists in inference optimization
- Developers working with edge AI or real-time systems
14 Hours
