Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN Optimization Capabilities

• How inference performance is managed within CANN
• Optimization targets for edge and embedded AI systems
• Understanding AI Core utilization and memory allocation strategies

Utilizing the Graph Engine for Analysis

• Introduction to the Graph Engine and its execution pipeline
• Visualizing operator graphs and monitoring runtime metrics
• Modifying computational graphs to drive optimization

Profiling Tools and Performance Metrics

• Applying the CANN Profiling Tool (profiler) for workload analysis
• Analyzing kernel execution times and identifying bottlenecks
• Memory access profiling and implementing 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
• Determining when and how to switch between TVM and TIK

Memory Optimization Techniques

• Managing memory layouts and buffer placement
• Techniques for reducing 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