Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN Optimisation Capabilities

• Mechanisms for handling inference performance within CANN.
• Optimisation objectives for edge and embedded AI systems.
• Comprehending AI Core utilisation and memory allocation strategies.

Leveraging the Graph Engine for Analysis

• Introduction to the Graph Engine and its execution pipeline.
• Visualising operator graphs and monitoring runtime metrics.
• Modifying computational graphs to enhance efficiency.

Profiling Tools and Performance Metrics

• Utilising the CANN Profiling Tool (profiler) for workload analysis.
• Identifying kernel execution time and pinpointing bottlenecks.
• Profiling memory access patterns and implementing tiling strategies.

Developing Custom Operators with TIK

• An overview of TIK and its operator programming model.
• Implementing custom operators using the TIK DSL.
• Testing and benchmarking operator performance metrics.

Advanced Operator Optimisation with TVM

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

Memory Optimisation Techniques

• Managing memory layout and buffer placement effectively.
• Strategies to minimise on-chip memory consumption.
• Best practices for asynchronous execution and resource reuse.

Real-World Deployment and Case Studies

• Case study: Performance tuning for a smart city camera pipeline.
• Case study: Optimising the inference stack for autonomous vehicles.
• Guidelines for iterative profiling and continuous improvement.

Summary and Next Steps