Optimizing Neural Network Performance with CANN SDK Training Course

Introduction to CANN Optimization Capabilities

• Mechanisms for handling inference performance within CANN
• Optimization targets for edge and embedded AI systems
• Concepts of AI Core utilization and memory allocation

Utilizing the Graph Engine for Analysis

• Overview of the Graph Engine and its execution pipeline
• Visualization of operator graphs and associated runtime metrics
• Adjusting computational graphs to achieve optimization goals

Profiling Tools and Performance Indicators

• Applying the CANN Profiling Tool for in-depth workload analysis
• Evaluating kernel execution times and identifying bottlenecks
• Profile memory access patterns and apply tiling strategies

Developing Custom Operators with TIK

• Introduction to TIK and its operator programming model
• Coding custom operators using the TIK DSL
• Testing and benchmarking the performance of new operators

Advanced Operator Optimization via TVM

• Introduction to TVM integration within CANN
• Auto-tuning methodologies for computational graphs
• Determining when to transition between TVM and TIK

Memory Optimization Strategies

• Control over memory layout and buffer positioning
• Methods to lower 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 inference stacks for autonomous vehicles
• Principles for iterative profiling and sustained improvement

Conclusion and Future Directions