Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN Optimization Capabilities

• How inference performance is managed within CANN
• Setting optimization objectives for edge and embedded AI systems
• Gaining insight into AI Core utilization and memory allocation

Leveraging the Graph Engine for Analysis

• Fundamentals of the Graph Engine and its execution pipeline
• Visualizing operator graphs alongside runtime metrics
• Refining computational graphs for performance gains

Profiling Tools and Performance Metrics

• Utilizing the CANN Profiling Tool (profiler) for workload assessment
• Evaluating kernel execution times to identify bottlenecks
• Profile memory access patterns and apply tiling strategies

Developing Custom Operators with TIK

• Introduction to TIK and its operator programming model
• Building a custom operator using the TIK DSL
• Validating and benchmarking operator efficiency

Advanced Operator Optimization using TVM

• Integrating TVM with the CANN ecosystem
• Employing auto-tuning strategies for computational graphs
• Determining when and how to switch between TVM and TIK

Memory Optimization Strategies

• Optimizing memory layout and buffer placement
• Methods to lower on-chip memory consumption
• Best practices for asynchronous execution and resource reuse

Real-World Deployments 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