Optimizing Neural Network Performance with CANN SDK Training Course

CANN Optimization Capabilities Overview

• Mechanisms of inference performance management within CANN
• Defining optimization targets for edge and embedded AI systems
• Analyzing AI Core usage and memory allocation strategies

Leveraging the Graph Engine for Analysis

• Fundamentals of the Graph Engine and its execution pipeline
• Visualizing operator graphs alongside runtime metrics
• Adjusting computational graphs to achieve optimization goals

Performance Metrics and Profiling Tools

• Applying the CANN Profiling Tool (profiler) for workload evaluation
• Assessing kernel execution times and identifying bottlenecks
• Profiling memory access patterns and implementing tiling strategies

Developing Custom Operators with TIK

• Fundamentals of TIK and its operator programming model
• Building a custom operator using the TIK DSL
• Validating and benchmarking operator performance

Advanced Operator Optimization via TVM

• Understanding the integration of TVM with CANN
• Implementing auto-tuning strategies for computational graphs
• Determining when and how to transition between TVM and TIK

Strategies for Memory Optimization

• Optimizing memory layouts and buffer placement
• Methods to lower on-chip memory consumption
• Best practices for asynchronous execution and resource reuse

Practical Deployment and Case Studies

• Case study: Tuning performance for smart city camera systems
• Case study: Enhancing the inference stack for autonomous vehicles
• Protocols for iterative profiling and sustained improvement

Recap and Future Directions