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Course Outline
Overview of CANN Optimization Capabilities
- Managing inference performance within CANN
- Setting 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 its execution pipeline
- Visualizing operator graphs and runtime metrics
- Modifying computational graphs to achieve optimization
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 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 on when and how to switch 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 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
- Robust comprehension of deep learning model architectures and training workflows
- Practical experience in deploying models using CANN, TensorFlow, or PyTorch
- Proficiency with the Linux CLI, shell scripting, and Python programming
Target Audience
- AI performance engineers
- Specialists in inference optimization
- Developers working on edge AI or real-time systems
14 Hours
