Optimizing Neural Network Performance with CANN SDK Training Course

Introduction to CANN Optimization Features

• Understanding how inference performance is managed within CANN
• Defining optimization targets for edge and embedded AI systems
• Explaining AI Core utilization and memory allocation mechanisms

Leveraging the Graph Engine for Analysis

• An introduction to the Graph Engine and its execution pipeline
• Visualizing operator graphs alongside runtime metrics
• Adjusting computational graphs to achieve optimization goals

Performance Metrics and Profiling Utilities

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

Building Custom Operators with TIK

• An overview of TIK and its operator programming model
• Creating a custom operator using the TIK DSL
• Conducting tests and benchmarks to measure operator performance

Advanced Operator Tuning with TVM

• An introduction to TVM integration within the CANN ecosystem
• Employing auto-tuning strategies for computational graphs
• Determining when and how to transition between TVM and TIK

Techniques for Memory Optimization

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

Real-World Applications and Case Studies

• Case study: Tuning performance for a smart city camera pipeline
• Case study: Optimizing the inference stack for autonomous vehicles
• Establishing guidelines for iterative profiling and continuous improvement

Conclusion and Future Directions