Optimizing Neural Network Performance with CANN SDK Training Course

Overview of CANN Optimization Capabilities

• Mechanisms for handling inference performance within CANN
• Defining optimization objectives for edge and embedded AI systems
• Grasping AI Core utilization and memory allocation strategies

Leveraging Graph Engine for Analysis

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

Profiling Tools and Performance Metrics

• Utilizing the CANN Profiling Tool for comprehensive workload analysis
• Assessing kernel execution times and identifying bottlenecks
• Profiling memory access patterns and implementing tiling strategies

Developing Custom Operators with TIK

• Introduction to TIK and its operator programming model
• Implementation of custom operators using the TIK DSL
• Testing and benchmarking the performance of developed operators

Advanced Operator Optimization with TVM

• Integrating TVM with the CANN ecosystem
• Applying auto-tuning strategies to computational graphs
• Determining when and how to transition between TVM and TIK

Memory Optimization Techniques

• Managing memory layouts and buffer placement effectively
• Strategies to minimize on-chip memory consumption
• Best practices for asynchronous execution and data reuse

Real-World Deployment and Case Studies

• Case study: Performance tuning for a smart city camera pipeline
• Case study: Optimizing the inference stack for autonomous vehicles
• Establishing guidelines for iterative profiling and continuous improvement

Summary and Next Steps