Optimizing Neural Network Performance with CANN SDK Training Course

Fundamentals of CANN Optimization

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

Analysis via Graph Engine

• Overview of the Graph Engine and its execution pipeline
• Visualizing operator graphs and runtime metrics
• Adjusting computational graphs to enhance optimization

Performance Metrics and Profiling Tools

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

Developing Custom Operators with TIK

• Overview of TIK and its operator programming model
• Building custom operators using the TIK DSL
• Testing and benchmarking operator efficiency

Advanced Operator Optimization using TVM

• Integrating TVM with the CANN ecosystem
• 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
• Strategies to minimize on-chip memory usage
• Best practices for asynchronous execution and resource reuse

Real-World 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 continuous enhancement

Wrap-up and Future Directions