Optimizing Neural Network Performance with CANN SDK Training Course

CANN Optimization Capabilities: An Overview

• Managing inference performance within the CANN ecosystem
• Defining optimization targets for edge and embedded AI applications
• Comprehending AI Core usage and memory allocation strategies

Leveraging the Graph Engine for Analysis

• Introduction to the Graph Engine and its execution pipeline
• Visualizing operator graphs and monitoring runtime metrics
• Refining computational graphs to enhance performance

Performance Metrics and Profiling Tools

• Applying the CANN Profiling Tool (profiler) for workload evaluation
• Assessing kernel execution times and identifying bottlenecks
• Profiling memory access patterns and applying tiling strategies

Developing Custom Operators with TIK

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

Advanced Operator Refinement with TVM

• Integrating TVM with the CANN framework
• Implementing auto-tuning strategies for computational graphs
• Determining the optimal balance between TVM and TIK usage

Memory Optimization Strategies

• Controlling memory layout and buffer placement
• Methods to minimize 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 systems
• Case study: optimizing inference stacks for autonomous vehicles
• Guidelines for iterative profiling and ongoing performance improvement

Conclusion and Future Directions