Optimizing Neural Network Performance with CANN SDK Training Course

Introduction to CANN Optimization Features

• Mechanisms for handling inference performance within CANN
• Key optimization targets for edge and embedded AI systems
• Concepts of AI Core usage and memory distribution

Analysis via the Graph Engine

• Fundamentals of the Graph Engine and its execution workflow
• Visualization of operator graphs and runtime metrics
• Adjusting computational graphs to enhance performance

Performance Metrics and Profiling Utilities

• Utilizing the CANN Profiling Tool for workload assessment
• Evaluating kernel execution times and identifying bottlenecks
• Profiling memory access patterns and implementing tiling strategies

Developing Custom Operators with TIK

• Overview of TIK and its operator programming framework
• Building a custom operator using the TIK DSL
• Validating and benchmarking operator efficiency

Advanced Operator Tuning with TVM

• Foundations of integrating TVM with CANN
• Strategies for auto-tuning computational graphs
• Determining when to switch between TVM and TIK

Techniques for Memory Optimization

• Controlling memory layout and buffer positioning
• Methods to minimize on-chip memory usage
• Best practices for asynchronous execution and data reuse

Practical Deployments and Case Studies

• Case study: Tuning performance for smart city camera pipelines
• Case study: Optimizing the inference stack for autonomous vehicles
• Guidelines for continuous profiling and iterative improvement

Conclusion and Future Directions