Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN Optimization Capabilities

• Managing inference performance within the CANN environment.
• Setting optimization targets for edge and embedded AI systems.
• Comprehending AI Core utilization and memory allocation strategies.

Leveraging Graph Engine for Analysis

• Introduction to the Graph Engine and its execution pipeline.
• Visualizing operator graphs and monitoring runtime metrics.
• Adjusting computational graphs to enhance optimization.

Profiling Tools and Performance Metrics

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

Custom Operator Development using TIK

• Overview of TIK and its operator programming model.
• Building a custom operator using the TIK DSL.
• Testing and benchmarking operator performance.

Advanced Operator Optimization with TVM

• Integrating TVM with CANN.
• Applying auto-tuning strategies to computational graphs.
• Determining the optimal approach for switching between TVM and TIK.

Memory Optimization Techniques

• Controlling memory layout and buffer placement.
• Strategies for minimizing on-chip memory usage.
• Best practices for asynchronous execution and resource reuse.

Real-World Deployment and Case Studies

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

Summary and Next Steps