Building Custom AI Operators with CANN TIK and TVM Training Course

Foundations of Custom Operator Development

• The rationale for custom operators: exploring use cases and technical constraints
• Analyzing the CANN runtime architecture and key integration points
• Positioning TBE, TIK, and TVM within the broader Huawei AI ecosystem

Low-Level Operator Programming with TIK

• Exploring the TIK programming model and its supported API landscape
• Managing memory and implementing tiling strategies within TIK
• The process of creating, compiling, and registering custom ops in CANN

Validation and Testing of Custom Operations

• Conducting unit and integration tests for ops within the execution graph
• Troubleshooting kernel-level performance bottlenecks
• Visualizing execution flows and buffer dynamics

Scheduling and Optimization via TVM

• Understanding TVM as a compiler framework for tensor operations
• Crafting custom operator schedules in TVM
• Executing TVM tuning, benchmarking, and code generation for Ascend

Framework and Model Integration

• Registering custom operations for MindSpore and ONNX compatibility
• Ensuring model integrity and managing fallback mechanisms
• Supporting complex multi-operator graphs with mixed precision capabilities

Real-World Case Studies and Advanced Optimizations

• Case study: Achieving high-efficiency convolution for small input dimensions
• Case study: Optimizing memory-aware attention operators
• Best practices for deploying custom ops across diverse devices

Wrap-Up and Future Directions