Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN Optimization Capabilities

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

Utilizing Graph Engine for Analysis

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

Profiling Tools and Performance Metrics

• Employing the CANN Profiling Tool (profiler) for workload analysis
• Examining kernel execution times and identifying bottlenecks
• Profiling memory access patterns and tiling strategies

Custom Operator Development with TIK

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

Advanced Operator Optimization with TVM

• Introduction to TVM integration with CANN
• Auto-tuning strategies for computational graphs
• Criteria for switching between TVM and TIK

Memory Optimization Techniques

• Managing memory layout and buffer placement
• Strategies to minimize on-chip memory consumption
• 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

Summary and Next Steps