Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN Optimization Features

• Mechanisms for handling inference performance within CANN
• Key optimization objectives for edge and embedded AI systems
• Insights into AI Core utilization and memory allocation strategies

Leveraging the Graph Engine for Analysis

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

Profiling Tools and Performance Indicators

• Utilizing the CANN Profiling Tool (profiler) for workload evaluation
• 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
• Building custom operators using the TIK DSL
• Validating and benchmarking operator performance

Advanced Operator Optimization via TVM

• Basics of integrating TVM with CANN
• Strategies for auto-tuning computational graphs
• Criteria for selecting between TVM and TIK implementations

Memory Optimization Strategies

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

Practical Deployment and Case Studies

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

Conclusions and Future Actions