Optimizing Neural Network Performance with CANN SDK Training Course

CANN Optimization Capabilities Explained

• Mechanisms of inference performance management in CANN
• Key optimization targets for edge and embedded AI systems
• Insights into AI Core utilization and memory allocation strategies

Leveraging Graph Engine for Analysis

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

Profiling Tools and Performance Metrics

• Applying the CANN Profiling Tool (profiler) for in-depth workload analysis
• Evaluating kernel execution times and identifying bottlenecks
• Memory access profiling and implementing tiling strategies

Custom Operator Development using TIK

• Overview of TIK and its operator programming model
• Building custom operators with the TIK DSL
• Validating and benchmarking operator performance

Advanced Operator Optimization with TVM

• Integrating TVM with the CANN ecosystem
• Employing auto-tuning strategies for computational graphs
• Decision-making frameworks for switching between TVM and TIK

Memory Optimization Techniques

• Strategic management of memory layout and buffer placement
• Methods 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 performance improvement

Conclusions and Forward-Looking Steps