Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN Optimization Features

• Managing inference performance within the CANN environment
• Defining optimization objectives for edge and embedded AI systems
• Grasping AI Core utilization and memory allocation principles

Leveraging Graph Engine for Analysis

• Fundamentals of the Graph Engine and its execution pipeline
• Visualizing operator graphs and monitoring runtime metrics
• Adjusting computational graphs to achieve optimal performance

Profiling Tools and Performance Indicators

• Applying the CANN Profiling Tool (profiler) for workload assessment
• Evaluating kernel execution times and identifying bottlenecks
• Profiling memory access patterns and implementing tiling strategies

Developing Custom Operators with TIK

• Introduction to TIK and its operator programming model
• Coding a custom operator using the TIK DSL
• Testing and benchmarking operator performance

Advanced Operator Optimization Using TVM

• Understanding TVM integration with CANN
• Applying auto-tuning strategies to computational graphs
• Determining when and how to transition between TVM and TIK

Memory Optimization Methods

• Controlling memory layout and buffer placement
• Strategies to minimize on-chip memory usage
• Best practices for asynchronous execution and data reuse

Real-World Deployment and Case Studies

• Case study: Performance tuning for smart city camera pipelines
• Case study: Optimizing the inference stack for autonomous vehicles
• Guidelines for iterative profiling and ongoing improvement

Conclusion and Future Directions