Optimizing Neural Network Performance with CANN SDK Training Course

Insight into CANN Optimization Capabilities

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

Analysis Using Graph Engine

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

Profiling Tools and Performance Metrics

• Utilizing the CANN Profiling Tool (profiler) for comprehensive workload analysis.
• Evaluating kernel execution times to identify bottlenecks.
• Memory access profiling and implementing tiling strategies.

Custom Operator Development via TIK

• Overview of 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

• Introduction to integrating TVM with CANN.
• Auto-tuning strategies for optimizing computational graphs.
• Guidelines for selecting between TVM and TIK based on context.

Memory Optimization Techniques

• Strategies for managing memory layout and buffer placement.
• Methods 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 smart city camera pipelines.
• Case study: Optimizing the inference stack for autonomous vehicles.
• Protocols for iterative profiling and continuous performance improvement.

Summary and Path Forward