Building Custom AI Operators with CANN TIK and TVM Training Course

Fundamentals of Custom Operator Development

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

Low-Level Operator Programming with TIK

• Exploring the TIK programming model and its associated APIs
• 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 testing of ops within the execution graph
• Troubleshooting and resolving kernel-level performance bottlenecks
• Analyzing operator execution flow and buffer dynamics

Scheduling and Optimization via TVM

• Understanding TVM as a specialized compiler for tensor operations
• Designing efficient schedules for custom operators in TVM
• Executing TVM tuning, benchmarking, and code generation optimized for Ascend

Framework and Model Integration

• Registering custom operators for compatibility with MindSpore and ONNX
• Ensuring model consistency and managing fallback behaviors
• Handling multi-operator graphs that utilize mixed precision

Practical Applications and Advanced Optimization

• Case study: Implementing high-efficiency convolutions for small input dimensions
• Case study: Optimizing attention operators with a focus on memory efficiency
• Best practices for deploying custom operators across diverse devices

Conclusion and Future Directions