Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN’s Optimization Capabilities

• Mechanisms for managing inference performance within CANN
• Performance goals for edge and embedded AI systems
• Insights into AI Core utilization and memory allocation

Leveraging the Graph Engine for Analysis

• Fundamentals of the Graph Engine and its execution pipeline
• Visualizing operator graphs and monitoring runtime metrics
• Adjusting computational graphs to drive optimization

Profiling Tools and Performance Indicators

• Employing the CANN Profiling Tool for workload analysis
• 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
• Crafting custom operators using the TIK DSL
• Conducting tests and benchmarking operator performance

Advanced Operator Optimization with TVM

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

Memory Optimization Strategies

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

Practical 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 enhancement

Conclusion and Future Directions