Optimizing Neural Network Performance with CANN SDK Training Course

Introduction to CANN Optimization Features

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

Utilizing Graph Engine for Analysis

• Fundamentals of the Graph Engine and its execution pipeline
• Methods for visualizing operator graphs and runtime metrics
• Strategies for modifying computational graphs to achieve optimization

Performance Metrics and Profiling Tools

• Applying the CANN Profiling Tool for comprehensive workload analysis
• Evaluating kernel execution time to identify bottlenecks
• Profiling memory access patterns and implementing tiling strategies

Developing Custom Operators with TIK

• Overview of TIK and its operator programming model
• Steps for implementing a custom operator using the TIK DSL
• Procedures for testing and benchmarking operator performance

Advanced Operator Optimization via TVM

• Basics of integrating TVM with the CANN ecosystem
• Auto-tuning methodologies for optimizing computational graphs
• Determining when and how to transition between TVM and TIK

Techniques for Memory Optimization

• Strategies for managing memory layout and buffer placement
• Methods to minimize on-chip memory consumption
• Best practices for asynchronous execution and data reuse

Real-World Deployments 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 continuous performance improvement

Conclusion and Future Directions