Building Custom AI Operators with CANN TIK and TVM Training Course

Introduction to Custom Operator Development

• Rationale for custom operators: use cases and technical constraints.
• CANN runtime architecture and operator integration points.
• Overview of TBE, TIK, and TVM within the Huawei AI ecosystem.

Low-Level Operator Programming with TIK

• Exploring the TIK programming model and its supported APIs.
• Memory management strategies and tiling approaches in TIK.
• Creating, compiling, and registering custom operations within CANN.

Testing and Validation of Custom Operations

• Conducting unit and integration tests for operations within the graph.
• Troubleshooting kernel-level performance bottlenecks.
• Visualizing operation execution flows and buffer behavior.

TVM-Driven Scheduling and Optimization

• Introduction to TVM as a compiler for tensor operations.
• Developing schedules for custom operations using TVM.
• TVM tuning, benchmarking, and code generation for Ascend devices.

Framework and Model Integration

• Registering custom operations for MindSpore and ONNX frameworks.
• Ensuring model integrity and managing fallback behaviors.
• Supporting multi-operator graphs with mixed precision capabilities.

Case Studies and Advanced Optimizations

• Case study: implementing high-efficiency convolution for small input shapes.
• Case study: memory-aware optimization of attention operators.
• Best practices for deploying custom operations across diverse devices.

Conclusion and Future Directions