Optimizing Neural Network Performance with CANN SDK Training Course

Fundamentals of CANN Optimization

• Processing inference performance within the CANN framework
• Setting optimization targets for edge and embedded AI systems
• Comprehending AI Core usage and memory allocation strategies

Analyzing with the Graph Engine

• Overview of the Graph Engine and its execution pipeline
• Visualizing operator graphs and runtime metrics
• Adjusting computational graphs to improve performance

Performance Metrics and Profiling Tools

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

Developing Custom Operators with TIK

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

Advanced Operator Refinement via TVM

• Integrating TVM with CANN
• Auto-tuning approaches for computational graphs
• Determining when and how to alternate between TVM and TIK

Memory Refinement Methods

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

Practical Deployment and Case Analyses

• Case analysis: tuning performance for a smart city camera pipeline
• Case analysis: refining the inference stack for autonomous vehicles
• Guidelines for iterative profiling and sustained improvement

Wrap-up and Future Directions