Optimizing Neural Network Performance with CANN SDK Training Course

CANN Optimisation Capabilities Overview

• How inference performance is managed within CANN
• Optimisation targets for edge and embedded AI systems
• Comprehending AI Core utilisation and memory allocation

Utilising the Graph Engine for Analysis

• Introduction to the Graph Engine and execution pipeline
• Visualising operator graphs and runtime metrics
• Refining computational graphs for optimal performance

Profiling Tools and Performance Metrics

• Applying the CANN Profiling Tool (profiler) for workload analysis
• Evaluating kernel execution time and identifying bottlenecks
• Memory access profiling and tiling strategies

Custom Operator Development with TIK

• Overview of TIK and its operator programming model
• Implementing custom operators using the TIK DSL
• Testing and benchmarking operator performance

Advanced Operator Optimisation with TVM

• Introduction to TVM integration with CANN
• Auto-tuning strategies for computational graphs
• Determining when and how to switch between TVM and TIK

Memory Optimisation Techniques

• Managing memory layout and buffer placement
• Techniques to minimise 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: Optimising the inference stack for autonomous vehicles
• Guidelines for iterative profiling and continuous improvement

Summary and Next Steps