Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN Optimization Capabilities

• Managing inference performance within the CANN framework
• Defining optimization targets for edge and embedded AI systems
• Comprehending AI Core utilization and memory allocation

Utilizing Graph Engine for Analysis

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

Profiling Tools and Performance Metrics

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

Developing Custom Operators 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
• Applying auto-tuning strategies to computational graphs
• Determining when and how to transition 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 inference stacks for autonomous vehicles
• Guidelines for iterative profiling and continuous improvement

Summary and Next Steps