Building Custom AI Operators with CANN TIK and TVM Training Course

Foundations of Custom Operator Development

• Rationale for custom operators: exploring use cases and architectural constraints
• Examining CANN runtime architecture and key operator integration touchpoints
• Contextualizing TBE, TIK, and TVM within the broader Huawei AI ecosystem

Implementing Low-Level Operators with TIK

• Grasping the TIK programming model and its supported API surface
• Managing memory and applying tiling strategies effectively in TIK
• The complete workflow: creating, compiling, and registering custom ops within CANN

Validation and Testing of Custom Operators

• Conducting unit and integration tests for operators within the execution graph
• Identifying and resolving kernel-level performance bottlenecks
• Visualizing operator execution flows and buffer memory behavior

Scheduling and Optimization via TVM

• Overview of TVM as a compiler framework for tensor operations
• Authoring schedules for custom operators using TVM
• Applying TVM for tuning, benchmarking, and code generation targeting Ascend

Framework and Model Integration

• Registering custom operators for compatibility with MindSpore and ONNX
• Ensuring model integrity and verifying fallback mechanisms
• Supporting multi-operator graphs that utilize mixed precision

Case Studies and Advanced Optimization Techniques

• Case study: Achieving high-efficiency convolution for small input shapes
• Case study: Optimizing attention operators with memory-awareness
• Best practices for deploying custom operators across diverse devices

Recap and Future Pathways