Optimizing Neural Network Performance with CANN SDK Training Course

Insights into CANN Optimization Capabilities

• How inference performance is managed within the CANN framework.
• Key optimization objectives for edge and embedded AI systems.
• Gaining clarity on AI Core utilization and memory allocation strategies.

Leveraging the Graph Engine for Analysis

• Overview of the Graph Engine and its execution pipeline.
• Visualizing operator graphs and interpreting runtime metrics.
• Adjusting computational graphs to achieve better performance.

Utilizing Profiling Tools and Key Metrics

• Employing the CANN Profiling Tool (profiler) for in-depth workload analysis.
• Examining kernel execution times to identify performance bottlenecks.
• Profiling memory access patterns and implementing tiling strategies.

Developing Custom Operators with TIK

• Overview of TIK and its operator programming model.
• Building custom operators using the TIK DSL.
• Validating and benchmarking the performance of new operators.

Advanced Operator Optimization via TVM

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

Advanced Memory Optimization Techniques

• Optimizing memory layout and buffer placement.
• Strategies to minimize on-chip memory consumption.
• Best practices for asynchronous execution and data reuse.

Real-World Deployment and Practical Case Studies

• Case study: Tuning performance for a smart city camera pipeline.
• Case study: Optimizing the inference stack for autonomous vehicles.
• Guidelines for iterative profiling and sustained performance improvement.