Optimizing Neural Network Performance with CANN SDK Training Course

Introduction to CANN Optimization Features

• How inference efficiency is managed within CANN.
• Key optimization targets for edge and embedded AI environments.
• Grasping AI Core usage and memory allocation patterns.

Leveraging Graph Engine for Analysis

• Overview of the Graph Engine and its execution pipeline.
• Visualizing operator graphs and runtime performance metrics.
• Adapting computational graphs to enhance performance.

Profiling Utilities and Performance Metrics

• Employing the CANN Profiling Tool for workload assessment.
• Evaluating kernel execution times and identifying bottlenecks.
• Analyzing memory access patterns and tiling approaches.

Building Custom Operators with TIK

• Overview of TIK and its operator programming paradigm.
• Developing a bespoke operator using the TIK DSL.
• Testing and benchmarking operator performance.

Advanced Operator Optimization via TVM

• Introduction to integrating TVM with CANN.
• Applying auto-tuning strategies to computational graphs.
• Determining when and how to transition between TVM and TIK.

Memory Optimization Methods

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

Practical Deployment and Case Studies

• Case study: performance tuning for smart city camera systems.
• Case study: optimizing the inference stack for autonomous vehicles.
• Guidelines for iterative profiling and continuous improvement.

Conclusion and Next Steps