Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN Optimization Features

• Handling of inference performance within the CANN framework.
• Optimization objectives for edge and embedded AI systems.
• Insights into AI Core utilization and memory allocation strategies.

Leveraging the Graph Engine for Analysis

• Introduction to the Graph Engine and its execution pipeline.
• Visualization of operator graphs and associated runtime metrics.
• Adapting computational graphs to facilitate optimization.

Profiling Tools and Key Performance Metrics

• Utilizing the CANN Profiling Tool for comprehensive workload analysis.
• Evaluating kernel execution times and identifying bottlenecks.
• Profiling memory access patterns and implementing tiling strategies.

Developing Custom Operators with TIK

• Overview of TIK and its operator programming model.
• Implementation of custom operators using the TIK DSL.
• Testing and benchmarking the performance of developed operators.

Advanced Operator Optimization via TVM

• Introduction to integrating TVM with CANN.
• Auto-tuning strategies applied to computational graphs.
• Determining when and how to transition between TVM and TIK.

Techniques for Memory Optimization

• Managing memory layouts and buffer placements.
• Methods to minimize on-chip memory usage.
• Best practices regarding asynchronous execution and resource reuse.

Real-World Deployment Scenarios 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.

Summary and Future Directions