Optimizing Neural Network Performance with CANN SDK Training Course

Introduction to CANN Optimization Capabilities

• Managing inference performance within the CANN framework
• Optimization objectives for edge and embedded AI systems
• Insights into AI Core utilization and memory allocation

Leveraging Graph Engine for Analysis

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

Profiling Tools and Performance Metrics

• Utilizing the CANN Profiling Tool (profiler) for workload evaluation
• Evaluating kernel execution time 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 performance

Advanced Operator Optimization using TVM

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

Memory Optimization Techniques

• Managing memory layout 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 a smart city camera pipeline
• Case study: optimizing the inference stack for autonomous vehicles
• Guidelines for iterative profiling and continuous improvement

Summary and Next Steps