Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN Optimization Capabilities

• How inference performance is managed within CANN.
• Key optimization objectives for edge and embedded AI systems.
• Comprehending AI Core utilization and memory allocation strategies.

Leveraging the Graph Engine for Analysis

• An introduction to the Graph Engine and its execution pipeline.
• Visualizing operator graphs alongside runtime metrics.
• Modifying computational graphs to achieve optimal performance.

Profiling Tools and Performance Metrics

• Utilizing the CANN Profiling Tool (profiler) for detailed workload analysis.
• Evaluating kernel execution times and identifying bottlenecks.
• Analyzing memory access patterns and tiling strategies.

Custom Operator Development with TIK

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

Advanced Operator Optimization with TVM

• Introduction to TVM integration within the CANN ecosystem.
• Auto-tuning strategies applied to computational graphs.
• Criteria and methods for switching between TVM and TIK.

Memory Optimization Techniques

• Managing memory layout and buffer placement effectively.
• Methods to minimize 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: Optimizing the inference stack for autonomous vehicles.
• Guidelines for iterative profiling and continuous performance improvement.

Summary and Future Directions