Optimizing Neural Network Performance with CANN SDK Training Course

CANN Optimization Capabilities Overview

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

Leveraging Graph Engine for Analysis

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

Profiling Tools and Performance Indicators

• Utilizing the CANN Profiling Tool (profiler) for workload assessment
• Evaluation of kernel execution times and identification of bottlenecks
• Profiling memory access patterns and applying tiling strategies

Custom Operator Creation via TIK

• Overview of TIK and its operator programming model
• Building custom operators using the TIK DSL
• Conducting tests and benchmarking operator performance

Advanced Operator Optimization using TVM

• Introduction to TVM integration with CANN
• Auto-tuning strategies applied to computational graphs
• Determining when and how to switch between TVM and TIK

Memory Optimization Strategies

• Controlling memory layout and buffer placement
• Methods to minimize on-chip memory usage
• Best practices for asynchronous execution and data reuse

Real-World Deployment 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 improvement

Wrap-up and Future Directions