Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN Optimization Capabilities

• How inference performance is managed within CANN
• Optimization objectives for edge and embedded AI systems
• Insights into AI Core utilization and memory allocation

Leveraging the Graph Engine for Analysis

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

Profiling Tools and Performance Metrics

• Utilizing the CANN Profiling Tool (profiler) for workload analysis
• Analyzing kernel execution time and identifying bottlenecks
• Memory access profiling and tiling strategies

Custom Operator Development using TIK

• Overview of TIK and its operator programming model
• Implementing custom operators with 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 and methods for switching between TVM and TIK

Memory Optimization Techniques

• Managing memory layout and buffer placement
• Techniques 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: Optimizing the inference stack for autonomous vehicles
• Guidelines for iterative profiling and continuous improvement

Summary and Next Steps