Optimizing Neural Network Performance with CANN SDK Training Course

Introduction to CANN Optimization Capabilities

• How CANN manages inference performance.
• Key optimization targets for edge and embedded AI systems.
• Understanding AI Core utilization and memory allocation strategies.

Leveraging Graph Engine for Analysis

• Fundamentals of the Graph Engine and its execution pipeline.
• Visualization of operator graphs and monitoring runtime metrics.
• Adjusting computational graphs to achieve optimal performance.

Profiling Tools and Performance Metrics

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

Developing Custom Operators with TIK

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

Advanced Operator Optimization using TVM

• Integrating TVM with the CANN ecosystem.
• Applying auto-tuning strategies to computational graphs.
• Determining when to switch between TVM and TIK for optimal results.

Memory Optimization Techniques

• Optimizing memory layout and buffer placement.
• Strategies to minimize 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.
• Establishing guidelines for iterative profiling and continuous improvement.

Summary and Future Steps