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Course Outline
Overview of CANN Optimization Capabilities
- How inference performance is managed within CANN
- Optimization objectives for edge and embedded AI systems
- Understanding AI Core utilization and memory allocation
Leveraging Graph Engine for Analysis
- Introduction to the Graph Engine and its execution pipeline
- Visualizing operator graphs and runtime metrics
- Modifying computational graphs for enhanced optimization
Profiling Tools and Performance Metrics
- Using 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 its operator programming model
- Implementing custom operators using TIK DSL
- Testing and benchmarking operator performance
Advanced Operator Optimization with TVM
- Introduction to TVM integration with CANN
- Auto-tuning strategies for computational graphs
- Criteria and methods for switching between TVM and TIK
Memory Optimization Techniques
- Managing memory layout and buffer placement
- Techniques 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 smart city camera pipelines
- Case study: optimizing inference stacks for autonomous vehicles
- Guidelines for iterative profiling and continuous improvement
Summary and Next Steps
Requirements
- Solid understanding of deep learning model architectures and training workflows
- Experience deploying models using CANN, TensorFlow, or PyTorch
- Familiarity with Linux CLI, shell scripting, and Python programming
Audience
- AI performance engineers
- Inference optimization specialists
- Developers focused on edge AI or real-time systems
14 Hours
