Optimizing Neural Network Performance with CANN SDK Training Course

CANN Optimization Capabilities: An Overview

• Mechanisms for handling inference performance within CANN
• Key optimization targets 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
• Visualization of operator graphs and runtime metrics
• Adapting computational graphs to enhance optimization

Profiling Tools and Key Performance Indicators

• Applying the CANN Profiling Tool for detailed workload analysis
• Evaluating kernel execution times and identifying bottlenecks
• Profiling memory access patterns and implementing tiling strategies

Developing Custom Operators with TIK

• Overview of TIK and its operator programming model
• Implementation of custom operators using the TIK DSL
• Validation and benchmarking of operator performance

Advanced Operator Optimization via TVM

• Introduction to TVM integration within the CANN ecosystem
• Auto-tuning strategies for computational graphs
• Strategic criteria for selecting between TVM and TIK

Memory Optimization Techniques

• Managing memory layouts and buffer placement
• Methods to minimize on-chip memory consumption
• Best practices for asynchronous execution and resource reuse

Real-World Deployment and Case Studies

• Case study: Performance tuning for smart city camera pipelines
• Case study: Optimization of autonomous vehicle inference stacks
• Guidelines for iterative profiling and continuous improvement

Conclusion and Future Directions