Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN Optimization Features

• Mechanisms for handling inference performance within CANN
• Optimization targets for edge and embedded AI systems
• Concepts of AI Core utilization and memory allocation

Utilizing the Graph Engine for Analysis

• Introduction to the Graph Engine and its execution pipeline
• Visualization of operator graphs and runtime metrics
• Modifying computational graphs to achieve optimization goals

Profiling Tools and Key Performance Metrics

• Applying the CANN Profiling Tool (profiler) for workload assessment
• Evaluating kernel execution times and identifying bottlenecks
• Memory access profiling and implementing tiling strategies

Developing Custom Operators with TIK

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

Advanced Operator Optimization via TVM

• Introduction to TVM integration with CANN
• Auto-tuning strategies applied to computational graphs
• Criteria for selecting between TVM and TIK

Memory Optimization Methods

• Managing memory layout and buffer positioning
• Methods to minimize on-chip memory consumption
• Best practices for asynchronous execution and data reuse

Real-World Deployment Scenarios 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 improvement

Summary and Subsequent Steps